system requirement specification

5/24/2018 System Requirement Specification

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BioMetric Criminals Identification System

Submitted By:

Abdul Bari Malik 905-FBAS/BSSE/F09

Asif Sharif 909-FBAS/BSSE/F09

ProjectSupervisor:

Mr. Syed Muhammad Saqlain

Assistant Professor

Department of Computer Science and Software Engineering

Faculty of Basic and Applied Sciences

International Islamic University, Islamabad

2013


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I n the name of Allah (SWT) who is most Beneficent and most

Merciful


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Faculty of Basic and Applied SciencesDepartment of Computer Sciences and Software Engineering

FINAL APPROVAL

Dated:

It is certified that we have read the project submitted by Abdul Bari and Asif Sharif and it

is our judgment that this project is of sufficient standard to warrant its acceptance by

International Islamic University, Islamabad for the Bachelors Degree in Software

Engineering.

COMMITTEE

External Examiner

Mr.Muhammad Nadeem

Assistant Professor,

DCS & SE, FBAS,


Internal ExaminerMr. Zulqarnain Hashmi

Lecturer,DCS & SE, FBAS,


Supervisor

Mr. Syed Muhammad Saqlain

Assistant ProfessorDCS & SE, FBAS,



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BCIS Dissertation

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A dissertation submitted to

DEPARTMENT OF COMPUTER SCIENCES

AND

SOFTWARE ENGNEERING

INTERNATIONAL ISLAMIC UNIVERSITY ISLAMABAD,

As partial fulfillment of the requirement of Bachelors degree in

Computer Sciences


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BCIS Dedication

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DEDICATION

This thesis is dedicated to our parents who have been a great source of motivation and

inspiration and supported us all the way since the beginning of our studies.


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BCIS Declaration

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DECLARATION

We hereby, declare that this project report, neither as a hole nor as a part there of has been

copied out from any source but in fact if any material has been used ,it is used as the

concept of original material taken from the web and all other references are mentioned for

subsequent sections. It is further declared that we have completed our work on the basis

of our personal efforts and under the sincere guidance of my teachers. If any part of this

part of work is proved to be copied out from any source, we will stand by the

consequences. No portion of Work presented in this report has been submitted in support

of any application for any degree or qualification of this or any other university.

Abdul Bari Malik

905-FBAS/ BSSE /F09

Asif Sharif

909-FBAS/ BSSE/ F09


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BCIS Acknowledgment

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ACKNOWLEDGEMENTS

To Him belongs the dominion of the Heaven and the Earth, it is He who gives life and

death and He has power over all things (Al-Quran).

First of all we would like to thank the Almighty Allah, who is invincible and He, who

blessed us with the abilities to complete this project. The completion of this project was

only dependent on His blessings. Whenever we faced any problem we always looked

forward towards Allah The Almighty for His blessings to help us in standing large and be

determinant in the problem.

There are a number of people without whom this project might not have been written, and

to whom we are greatly indebted.

We Especially Thanks to our parents who helped us in our most difficult times and it is

due to their untiring effort that we are at this position.

We would also like to thanks all our Professors throughout our Bachelors course for

giving us this opportunity to learn and grow towards attainment of this degree.

Whenever one has a difficult task ahead, the first problem is how you climb and see

through the first stair because after the first step ahead, one always tries to move up and

up. So when we decided to take Biometric Person Identification using Face

Recognition as our final project this was our move towards the first stair and we were

fully supported to step up on the first stair by our teacher Mr. Syed Muhammad Saqlain

who later wore the gown of our project supervisor. Without his instructions and support

we wouldnt be able to move towards our destination.

Apart from the above honorable personalities we owe loving thanks to all our batch mates

who supported us and always gave us new ideas to include and were there as our

CRITICS, which helped us to climb till the last stair.

Finally, we would like to thank everybody who was important to the successful

realization of project, as well as expressing our apology that we could not mention

personally one by one.

Abdul Bari Malik

905-FBAS/ BSSE /F09

Asif Sharif

909-FBAS/ BSSE/ F09


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BCIS Abstract

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PROJECT IN BRIEF

Project Title: BioMetricCriminals Identification System

Objective: To develop a person identification system along with

their profiles on the basis of their faces

Undertaken By: Abdul Bari

Asif Sharif

Supervised By: Mr. Syed Muhammad Saqlain

Date Started: April 8,2013

Date Completed: December 29, 2013

Language: JAVA

Tool(s) Used: Eclipse IDE

Microsoft Office 2010

Sparx Enterprise Architect 7.5

Visual Paradigm for UML 11

Operation System: Microsoft Windows7

System Used: Processor Dual Core, 2.80GH


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BCIS Abstract

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ABSTRACT

This system is able to verify a persons identity through his/her facial features. This is

achieved by using Eigen faces, Principal Component Analysis (PCA) and OpenCV

Object Detection.

Persons are enrolled into the system by providing their personal information and frontal

face samples. These face samples are verified by detecting face in the face sample and

then preprocessing is performed on them. Once the persons are enrolled, the system is

trained on the preprocessed face samples using PCA (Principle Component Analysis).

Now it can be used to recognize persons, the person to be recognized comes in front ofcamera, his/her face sample is taken, preprocessed and compared with the enrolled

images and the result is returned as matched or not matched.


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BCIS Table- of-Contents

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Table of Contents

CHAPTER 1 ........................................................................................................................ 11.

Introduction .................................................................................................................. 2

1.1. Problem Statement ................................................................................................ 21.2. Purpose .................................................................................................................. 31.3. Scope ..................................................................................................................... 31.4. Definition, Acronyms, and Abbreviations ............................................................ 31.5. Objective of the Project ......................................................................................... 31.6. Positioning ............................................................................................................. 3

1.6.1. Problem Statement ......................................................................................... 31.7. Main Project Achievements .................................................................................. 41.8. Product Overview .................................................................................................. 4

1.8.1. Product Perspective ........................................................................................ 41.8.2. Assumptions and Dependencies .................................................................... 41.8.3. Cost and Pricing ............................................................................................. 41.8.4. System Model ................................................................................................ 5...................................................................................................................................... 5

1.9. Product Features .................................................................................................... 51.9.2. User Characteristics ....................................................................................... 61.9.3. Functional Requirement ................................................................................. 61.9.4. Organization of Report .................................................................................. 61.9.5. Tools and Technologies ................................................................................. 7

CHAPTER 2 ........................................................................................................................ 82. Literature Survey ......................................................................................................... 9

2.1. Projects Developed ................................................................................................ 92.1.1. ImageWare Software, Inc. ............................................................................. 92.1.2. VeriLook SDK (Face identification for stand-alone or Web applications) . 102.1.3. TrueFace ...................................................................................................... 102.1.4. FaceSDK ...................................................................................................... 102.1.5. Intelligent vision systems, Inc...................................................................... 112.1.6. FaceIt Pc....................................................................................................... 11

2.2. Techniques for Face Recognition ........................................................................ 12


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2.2.1. Geometric Features ...................................................................................... 122.2.2. Eigen Faces .................................................................................................. 122.2.3. Graph Matching ........................................................................................... 132.2.4. Fisher Faces (Linear Discriminate Analysis algorithm) .............................. 132.2.5. Template Matching ...................................................................................... 142.2.6. Neural Network Approach ........................................................................... 142.2.7. Stochastic Modeling..................................................................................... 162.2.8. N-tuple classifiers ........................................................................................ 16

2.3. Basic Concepts .................................................................................................... 182.3.1. Preprocessing ............................................................................................... 182.3.2. Object Detection .......................................................................................... 182.3.3. Face Recognition ......................................................................................... 19

3. Analysis...................................................................................................................... 293.1. Use Cases Model ................................................................................................. 29

3.1.1. Use Case Diagram........................................................................................ 293.1.2. Use Cases Descriptions in Brief .................................................................. 303.1.3. Use Case Description in Detail .................................................................... 313.1.4. System Sequence Diagrams ......................................................................... 35

3.2. Domain / Conceptual Model ............................................................................... 393.3. Activity Diagram ................................................................................................. 40

3.3.1. Admin Side Activity Diagram ..................................................................... 403.3.2. User Side Activity Diagram ......................................................................... 41

CHAPTER 4 ...................................................................................................................... 424. System Design ........................................................................................................... 43

4.1. Class Diagram ..................................................................................................... 444.2. Entity Relationship Diagram ............................................................................... 454.3. Sequence Diagrams ............................................................................................. 46

4.3.1. SD1: Enrollment .......................................................................................... 474.3.2. SD2: Preprocessing ...................................................................................... 474.3.3. SD3: Training............................................................................................... 484.3.4. SD4: Testing ................................................................................................ 48

CHAPTER 5 ...................................................................................................................... 495. Implementation .......................................................................................................... 50


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5.1. Tools and Technology ......................................................................................... 515.2. Deployment Diagram .......................................................................................... 51

CHAPTER 6 ...................................................................................................................... 526. Software Testing ........................................................................................................ 53

6.1. Why is Software Tested?..................................................................................... 556.1.1. To Improve Quality: .................................................................................... 556.1.2. For Verification and Validation (V&V) ...................................................... 556.1.3. For Reliability Estimation ............................................................................ 55

6.2. Software Testing Techniques .............................................................................. 566.2.1. Test Case Design.......................................................................................... 566.2.2. White Box Testing ....................................................................................... 566.2.3. Basic Path Testing........................................................................................ 566.2.4. Regression Testing ....................................................................................... 566.2.5. Control Structure Testing ............................................................................. 576.2.6. Black Box Testing........................................................................................ 57

6.3. Software Testing Strategies ................................................................................. 576.3.1. Unit Testing ................................................................................................. 576.3.2. Integration Testing ....................................................................................... 576.3.3. Function Testing .......................................................................................... 586.3.4. System Testing ............................................................................................. 586.3.5. Acceptance Testing ...................................................................................... 58

6.4. BPIS Testing ....................................................................................................... 586.5. Test case .............................................................................................................. 586.6. Deriving Test Cases ............................................................................................ 596.7. Test Case Description.......................................................................................... 60

6.7.1. Loading of Menu.......................................................................................... 606.7.2. Enrollment Test Case ................................................................................... 606.7.3. User Input handling...................................................................................... 616.7.4. Face Detection ............................................................................................. 616.7.5. Database Inserting ........................................................................................ 626.7.6. Training ........................................................................................................ 626.7.7. Testing.......................................................................................................... 63

7. Conclusion ................................................................................................................. 65


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8. User Manual ............................................................................................................... 73Pc Minimum System Requirements............................................................................... 73

8.1. Main Window ...................................................................................................... 738.2. Enrollment Wizard Window 1 ............................................................................ 748.3. Enrollment Wizard Window 2 ............................................................................ 758.4. Recognition Window........................................................................................... 76

References .......................................................................................................................... 78


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Table of Figures

Figure 2.1: Flowchart of Face Recognition using Eigen Faces ......................................... 22Figure 3.1: Use Case Diagram ........................................................................................... 29Figure 3.2: SSD1 Enrollment ............................................................................................. 37Figure 3.3: SSD2 Preprocessing ........................................................................................ 37Figure 3.4: SSD3 Training ................................................................................................. 38Figure 3.5: SSD4 Testing ................................................................................................... 38Figure 3.6: Domain Model ................................................................................................. 39Figure 3.7: Admin Side Activity Diagram ......................................................................... 40Figure 3.8: User Side Activity Diagram ............................................................................ 41Figure 4.1: Class Diagram ................................................................................................. 44Figure 4.2: Entity Relationship Diagram ........................................................................... 45Figure 4.3: Sequence Diagram of Enrollment ................................................................... 47Figure 4.4: Sequence Diagram of Preprocessing ............................................................... 47Figure 4.5: Sequence Diagram of Training ........................................................................ 48Figure 4.6: Sequence Diagram of Testing ......................................................................... 48Figure 5.1: Deployment Diagram ...................................................................................... 51

Table of Tables

Table 6.1: Main Menu Test Case ....................................................................................... 60Table 6.2: Enrollment Test Case ........................................................................................ 60Table 6.3:User Input Handling Test Case .......................................................................... 61Table 6.4: Face Detection Test Case.................................................................................. 61Table 6.5: Database Inserting Test Case ............................................................................ 62Table 6.6: Training Test Case ............................................................................................ 62Table 6.7: Testing Test Case .............................................................................................. 63


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Chapter 1 Introduction

BCIS 1

CHAPTER 1

Introduction


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BCIS 2

1. IntroductionThe capacity of human being to recognize faces is quite remarkable. It is nearly automatic

and so intelligent that it persists even when the shape of a persons face changes with the

passage of time. We are supposed to bring this property of Natural Intelligence in

Artificial Intelligence so that we could recognize faces electronically. There are no built

in features in computers to perform this task, we have to program it.

There are many approaches used for the identification of person in which the most

common are Username and Password to login a Personal Computer, PIN (Person

Identification Number) Code systems. For example ATMs, Token systems such as

National ID Card number or vehicles license etc. But there were many problems with

these methods like losing card, theft, forgetting password, cards getting washed out and

many other problems like this. Due to these problems, there has been developed

considerable interest in biometric identification systems, which use pattern characters.

1.1. Problem StatementConsidering of an image representing a frame taken from video stream, automatic face

recognition is a particularly complex task that involves detection and location of faces in

a cluttered background followed by normalization and recognition. The human face is avery challenging pattern to detect and recognize, because while its anatomy is rigid

enough so that all faces have the same structure, at the same time there are a lot of

environmental and personal factors affecting facial appearance. The main problem of face

recognition is large variability of the recorded images due to pose, illumination

conditions, facial expressions, use of cosmetics, different hairstyle, presence of glasses,

beard, etc. Images of the same individual taken at different times, may sometimes exhibit

more variability due to the aforementioned factors (intrapersonal variability), than images

of different individuals due to gender, race, age and individual variations (extra personal

variability). One way of coping with intrapersonal variations is including in the training

set images with such variations. And while this is a good practice for variations such as

facial expressions, use of cosmetics and presence of glasses or beard, it may not be

successful in case of illumination or pose variations.


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BCIS 3

1.2. PurposeThis project is aimed to identify the persons. Here the technique is we already store some

images of the person in our database along with his details. These images are stored in

database record so to identify any person. If any image is matched then we predict that heis only the required person. Thus using this project it provides a very friendly

environment for users to identify persons very easy.

1.3. ScopeThe scope of the project is confined to store the image. When a person has to be identified

the images stored in the database are compared with the existing details.

1.4. Definition, Acronyms, and AbbreviationsBPIS: Biometric Person Identification System

1.5. Objective of the ProjectThe aim of this project is to come up with a simple and improved implementation for

person identification using personal computers. In this project, we have used for the

algorithms Eigen Faces and Principal Component Analysis (PCA) to determine

similarities between faces which consider different poses of a person and also to maintainthe profiles of the persons along with their faces.

1.6. Positioning1.6.1. Problem Statement

The Problem

of

Manually Identify the persons.

Affects Security agencies and investigation departments (employees).

The Impact of

which isAdministration needs to manually manage the record of the

persons and its hard and time consuming to identify the persons.

A successful

solution wouldbe

Proposed System will automatically manage the information of the

persons. And identify the persons by biometric techniques.


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BCIS 4

1.7. Main Project AchievementsThe project achievements can be stated as follows:

Adaptation of two algorithms in JAVA that compute similarity between faces.PCA and Eigen Faces.

Insertion, Deletion and Updating of user profiles along with their faces. Development of a Graphical User Interface which allows user to carry out

experiment to test the system.

A set of experiment carried out with 4 different pictures of 5 to 10 differentsubjects and found the recognition result of the system to be 80%.

1.8. Product OverviewThe general description comprises of following phases

1.8.1. Product PerspectiveThe product is a standalone application which is proposed in desktop based, and a

windows service for executing product. It integrates with MySQL on one end, and

interfaces with a GUI package to provide the visual content. In between, business logic is

involved.

1.8.2. Assumptions and DependenciesAssuming that user must have Camera attached with the system whether it was a built-in

Webcam or separate USB Cam.

1.8.3. Cost and PricingTo be decided.


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BCIS 5

1.8.4. System ModelThe following figure shows the system model.

1.9. Product FeaturesFollowing are the brief descriptions of the component of the system model.

1.9.1.1. EnrollmentIn this process, users personal info is entered and stored in the data base. Users frontal

face samples are taken. Face samples are verified through Face Detection. User is

provided with an option to save a sample. At least four samples of a user are taken.

1.9.1.2. TrainingClasses are computed and Eigen faces of classes are generated that were used as face

space for recognition process.

User Info Enrollment Face Samples

PCA

Eigen Vectors

Preprocessing

Eigen Values

Training

Recognition


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BCIS 6

1.9.1.3. RecognitionFace sample is taken of the person to be recognized. Again the face sample is verified

through Face Detection. Face sample is preprocessed. Similarity scores are computed and

the best match is found.

1.9.2. User CharacteristicsRunning this software does not require any advance knowledge of computer. This

software is very user friendly so any person that has basic knowledge of computer can use

this software.

1.9.3. Functional RequirementFollowing are some basic requirements are described briefly:

Enrollment Taking the snapshots of the user Detecting face in the snapshot Maintaining the profile

Administration of user profile Insert Update Delete

Identification Taking the snapshot Detecting face Recognizing face

1.9.4. Organization of ReportChapter 2 contains the literature reviewed for the BPIS system. Chapter 3 explains the

basic concepts which include preprocessing, Object Detection and Face Recognition.

Chapter 4 explains Analysis and Design in BPIS system which has Use Cases, Use Case

Diagram, SSD (System Sequence Diagram), Domain Model and Class Diagram. Chapter

5 describes the Implementation Details; Chapter 6 describes the testing mechanisms.

Chapter 7 contains user manual. Chapter 8 and Chapter 9 tell us about challenges and

further issues in the development of project. Appendix A contains background

mathematics that is needed to understand the algorithms.


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BCIS 7

1.9.5. Tools and TechnologiesThe main tools and technologies used in the development of BPIS are:

Eclipse IDE SQL server Web Camera SPARX Enterprise Architect


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Chapter 2 Literature Survey & Basic Concepts

BCIS 8

CHAPTER 2

Literature Survey

And

Basic Concepts


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BCIS 9

2. Literature SurveyThere is a lot of study done for the development of this project. There are many projects

developed in the area of biometrics and many techniques used for Face Recognition.

2.1. Projects Developed2.1.1. ImageWare Software, Inc.ImageWare Systems, Inc. (IWS) is a leader of cutting-edge, identity-based, credential

management solutions driven by biometric technology

Delivering next-generation biometrics as an interactive and Scalable cloud-based

solution

IWS brings together cloud and mobile technology to offer multi-factor authentication for

smart phone users and mobile clients. IWS supports multi-modal biometric authentication

including voice, fingerprint, and facial recognition. All of which can be combined

alongside other authentication and access control facilities including tokens, digital

certificates, passwords, and PINS, to provide the ultimate level of assurance and

accountability for corporate networks and Web applications from mobile phones and

tablets to PC desktop environment.

Key solution offerings:

Biometric Enginee 2.0

This is a scalable, agnostic multibiometric identity management solution that ensures only

valid individuals gain access to controlled areas or attains secure documents.

It is agnostic in biometric algorithm and hardware and can manage tens of millions of

individuals using standard off the-shelf hardware, minimizing the cost of ownership.

GoMobile interactive

It is a platform allowing any mobile application to be secured with biometric

identification. It enables a range of unprecedented activities from secure sharing of

sensitive information to the realization of a true mobile wallet.

The biometrics-based authentication gives users the confidence that their personal

information is secure, while the push marketing capabilities allow companies unparalleled

interactivity that can be personalized to the needs and interests of their customers.


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2.1.2. VeriLook SDK (Face identification for stand-alone or Webapplications)

NEUROtechnology has developed a face recognition algorithm. VeriLook facial

identification technology is designed for biometric systems developers and integrators.

The technology assures system performance and reliability with live face detection,

simultaneous multiple face recognition and fast face matching in 1-to-1 and 1-to-many

modes.

VeriLook is available as a software development kit that allows development of stand-

alone and Web-based solutions on Microsoft Windows, Linux, Mac OS X and Android

platforms.

VeriLook algorithm implements advanced face localization, enrollment and matching

using robust digital image processing algorithms with the same high recognition quality

on PCs, embedded and mobile devices. The algorithm is able to perform simultaneous

multiple face processing in a single frame, as well as live face detection and gender

classification. Facial feature points can be optionally extracted.

2.1.3. TrueFaceThis software is developed at Miros, Inc. Companys software allows for face recognition

through use of a video camera and a computer; site provides free demo of products to

demonstrate how your face can be used to access secure web pages; included its

information on TrueFace, a biometric that eliminates cards for check cashing ATMs.

2.1.4. FaceSDKFaceSDK is a software development kit that lets you add facial recognition

featuresto your desktop and web applications.

Compatible with 32- and 64-bit apps in multiple platforms (Windows, Mac and Linux)

FaceSDK enables you to use facial recognition technologyin your software, so that you

can develop apps to authenticate users with webcams, find matching faces on multiple

images, recognize people in graphic editors or create morphing and animation effects.
http://facesdk.en.softonic.com/http://facesdk.en.softonic.com/


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FaceSDK can be used both on online and desktop applications, anywhere where accurate

facial recognition is required from funny face animation effects to biometric

authentication for high-security systems.

This SDK includes several samples in different programming languages (Microsoft

Visual C++, C#, Visual Basic and more) for testing purposes, as well as a demo to test all

the SDKs possibilities right away.

2.1.5. Intelligent vision systems, Inc.Product focus on face recognition (searches in data bases of faces), face verification

(verification of identity of a single individual), electronic look control (software activates

an electronic door lock mechanism via printer port and simple interface), and

ATM/secure access/computer security applications

2.1.6. FaceIt PcThis software is developed at Visionics Corporation. Information on the companys

biometric recognition system (a software engine that automatically detects, locates and

identifies human faces from live video or static images) is provided; company focuses on

building applications for banking, Intranet security, surveillance, time & attendance, law

enforcement, national IDs, application security, multimedia search, passport control,

service kiosks, entitlement programs, voter registration, enterprise security, database

access, teleconferencing, airport/luggage security, border control, consumer research, and

vehicular sentry.


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BCIS 12

2.2. Techniques for Face RecognitionFollowing are the techniques used for the purpose of Face Recognition system.

2.2.1. Geometric FeaturesMany people have explored geometric feature based methods for face recognition.

Kanade presented an automatic feature extraction method based on ratios of distances and

reported a recognition rate of 45-75% with data base of 20 people. Burnelli and Paggio

compute a set of geometrical features such as nose width and length, mouth position, and

chin shape. They report a 90% recognition rate on a database of 47 people. However they

show that a simple template matching scheme provides 100% recognition for the same

database. Cox et al. have recently introduced a mixture-distance technique which

achieves a recognition rate of 95% using a query database of 95 images from a total of

685 images. Each face is represented by 30 manually extracted distances.

Systems, which employee precisely measured distances between features, may be most

useful for finding possible matches in a large mug-shot database. For other applications

automatic identification of these points would be required, and the resulting system would

be dependent on the accuracy of the feature location algorithm.

2.2.2. Eigen FacesEigenfacesis the name given to a set ofeigenvectors when they are used in thecomputer

vision problem of human face recognition. The approach of using eigenfaces for

recognition was developed by Sirovich and Kirby (1987)and used by Matthew Turk

and Alex Pentland in face classification. The eigenvectors are derived from the

covariance matrix of theprobability distribution over the high-dimensionalvector space

of face images. The eigenfaces themselves form a basis set of all images used to construct

the covariance matrix. This produces dimension reduction by allowing the smaller set of

basis images to represent the original training images. Classification can be achieved by

comparing how faces are represented by the basis set.

Turk and Pentland present results on a database of 16 subjects with various head

orientation, scaling and lighting. Their images appear identical otherwise with little

variation in facial expression, facial details, pose etc. For lighting, orientation, and scale

variation their system achieves 96%, 85% and 64% correct classification respectively.

Scale is normalized to the eigenface size based on an estimate of their head size. The
http://en.wikipedia.org/wiki/Eigenvectorhttp://en.wikipedia.org/wiki/Computer_visionhttp://en.wikipedia.org/wiki/Computer_visionhttp://en.wikipedia.org/wiki/Facial_recognition_systemhttp://en.wikipedia.org/wiki/Facial_recognition_systemhttp://en.wikipedia.org/w/index.php?title=Matthew_Turk&action=edit&redlink=1http://en.wikipedia.org/w/index.php?title=Matthew_Turk&action=edit&redlink=1http://en.wikipedia.org/wiki/Alex_Pentlandhttp://en.wikipedia.org/wiki/Alex_Pentlandhttp://en.wikipedia.org/wiki/Eigenvectorshttp://en.wikipedia.org/wiki/Covariance_matrixhttp://en.wikipedia.org/wiki/Probability_distributionhttp://en.wikipedia.org/wiki/Dimensionhttp://en.wikipedia.org/wiki/Vector_spacehttp://en.wikipedia.org/wiki/Vector_spacehttp://en.wikipedia.org/wiki/Dimensionhttp://en.wikipedia.org/wiki/Probability_distributionhttp://en.wikipedia.org/wiki/Covariance_matrixhttp://en.wikipedia.org/wiki/Eigenvectorshttp://en.wikipedia.org/wiki/Alex_Pentlandhttp://en.wikipedia.org/w/index.php?title=Matthew_Turk&action=edit&redlink=1http://en.wikipedia.org/wiki/Facial_recognition_systemhttp://en.wikipedia.org/wiki/Facial_recognition_systemhttp://en.wikipedia.org/wiki/Computer_visionhttp://en.wikipedia.org/wiki/Computer_visionhttp://en.wikipedia.org/wiki/Eigenvector


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BCIS 13

middle of the faces is accentuated, reducing any negative affect of changing hairstyle and

backgrounds.

2.2.3. Graph MatchingAn approach to face recognition is well-known method of Graph matching. Lades et al.

present dynamic link Architecture for distortion invariant object recognition, which

employs elastic graph matching to find the closest stored graph. Objects are presented

with spare graphs whose vertices are labeled with a multi-resolution description in terms

of a local power spectrum, and whose edges are labeled with geometric distances. They

present good results with a database of 87 people and test images composed of different

expressions and faces turned 15 degrees. The matching process is computationally

expensive, taking roughly 25 seconds to compare an image of 87 stored objects when

using a parallel machine with 23 transputers. Wishok et al. use an updated version of the

technique and compare 300 faces against 300 different faces of same people taken from

the FERET database. They report a recognition rate of 97.3%.

2.2.4. Fisher Faces (Linear Discriminate Analysis algorithm)Linear Discriminate Analysis algorithm is also used in face recognition. Originally

developed in 1936 by R.A. Fisher, Discriminant Analysis is a classic method of

classification that has stood the test of time. Discriminant analysis often produces models

whose accuracy approaches (and occasionally exceeds) more complex modern methods.

LDA is similar to Principal Component Analysis (PCA). In both PCA and LDA we look

for the linear combinations of the variables which explain the given data well. LDA

explicitly attempts to model the difference between the classes of data. PCA on the other

hand does not take into account any difference in class but builds the feature

combinations based on differences rather than similarities.

In a computer based face recognition system, each face is represented by a large number

of pixel values. Linear discriminant analysis is primarily used here to reduce the number

of features to a more manageable number before classification. Each of the new

dimensions is a linear combination of pixel values, which form a template. The linear

combinations obtained using Fisher's linear discriminant are called Fisher Faces.


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BCIS 14

2.2.5. Template MatchingA simple version of template matching is that a test image represented as a two

dimensional array of intensity values is compared using a suitable metric, such as the

Euclidean distance, with a single template representing the whole face. There are severalother more sophisticated versions of template matching on face recognition. One can use

more than one face template from different viewpoints to represent an individual's face. A

face from a single viewpoint can also be represented by a set of multiple distinctive

smaller templates. The face image of gray levels may also be properly processed before

matching. In, Bruneli and Poggio automatically selected a set of four features templates,

i.e., the eyes, nose, mouth, and the whole face, for all of the available faces. They

compared the performance of their geometrical matching algorithm and template

matching algorithm on the same database of faces, which contains 188 images of 47

individuals.

The template matching was superior in recognition (100 percent recognition rate) to

geometrical matching (90 percent recognition rate) and was also simpler. Since the

principal components (also known as eigenfaces or eigenfeatures) are linear combinations

of the templates in the data basis, the technique cannot achieve better results than

correlation, but it may be less computationally expensive. One drawback of template

matching is its computational complexity. Another problem lies in the description of

these templates. Since the recognition system has to be tolerant to certain discrepancies

between the template and the test image, this tolerance might average out the differences

that make individual faces unique. In general, template-based approaches compared to

feature matching are a more logical approach.

2.2.6. Neural Network ApproachThe attractiveness of using neural network could be due to its non-linearity in the

network. Hence, the feature extraction step may be more efficient than the linear

Karhunen-LoeAve methods. One of the first artificial neural network (ANN) techniques

used for face recognition is a single layer adaptive network called WISARD which

contains a separate network for each stored individual. The way of constructing a neural

network structure is crucial for successful recognition. It is very much dependent on the

intended application.


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For face detection, multilayer perceptron and convolutional neural network havebeen applied.

For face verification, Cresceptron multi resolution pyramid structure is used.Lawrence et al. proposed a hybrid neural network, which combined local image sampling,

a self-organizing map (SOM) neural network, and a convolutional neural network. The

SOM provides a quantization of the image samples into a topological space where inputs

that are nearby in the original space are also nearby in the output space, thereby providing

dimension reduction and invariance to minor changes in the image sample. The

convolutional network extracts successively larger features in a hierarchical set of layers

and provides partial invariance to translation, rotation, scale, and deformation. The

authors reported 96.2 percent correct recognition on ORL database of 400 images of 40

individuals. The classification time is less than 0.5 second, but the training time is as long

as 4 hours. Lin et al used probabilistic decision-based neural network (PDBNN), which

inherited the modular structure from its predecessor, a decision based neural network

(DBNN).

The PDBNN can be applied effectively to

1) Face detector:This finds the location of a human face in a cluttered image,

2) Eye localizer:This determines the positions of both eyes in order to generatemeaningful feature vectors, and

3) Face recognizer:A hierarchical neural network structure with non-linear basisfunctions and a competitive credit-assignment scheme.

PDBNN-based biometric identification system has the merits of both neural networks and

statistical approaches, and its distributed computing principle is relatively easy to

implement on parallel computer. In, it was reported that PDBNN face recognizer had the

capability of recognizing up to 200 people and could achieve up to 96 percent correct

recognition rate in approximately 1 second. However, when the number of persons

increases, the computing expense will become more demanding. In general, neural

network approaches encounter problems when the number of classes (i.e., individuals)

increases. Moreover, they are not suitable for a single model image recognition task

because multiple model images per person are necessary in order for training the systems

to optimal parameter setting.


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2.2.7. Stochastic ModelingStochastic modeling of non-stationary vector time series based on hidden Markov models

(HMM) has been very successful for speech applications. Samaria and Fallside applied

this method to human face recognition. Faces were intuitively divided into regions suchas the eyes, nose, mouth, etc., which can be associated with the states of a hidden Markov

model. Since HMMs require a one-dimensional observation sequence and images are two

dimensional, the images should be converted into either 1D temporal sequence or 1D

spatial sequence. In, a spatial observation sequence was extracted from a face image by

using a band sampling technique. Each face image was represented by a 1D vector series

of pixel observation. Each observation vector is a block of L lines and there is an M lines

overlap between successive observations. An unknown test image is first sampled to an

observation sequence. Then, it is matched against every HMMs in the model face

database (each HMM represents a different subject). The match with the highest

likelihood is considered the best match and the relevant model reveals the identity of the

test face. The recognition rate of HMM approach is 87 percent using ORL database

consisting of 400 images of 40 individuals. Pseudo 2D HMM was reported to achieve a

95 percent recognition rate in their preliminary experiments. Its classification time and

training time were not given (believed to be very expensive). The choice of parameters

had been based on subjective intuition.

2.2.8. N-tuple classifiersConventional n-tuple systems have the desirable features of super-fast single-pass

training, super-fast recognition, conceptual simplicity, straightforward hardware and

software implementations and accuracy that is often competitive with other more

complex, slower methods. Due to their attractive features, n-tuple methods have been the

subject of much research. In conventional n-tuple based image recognition systems, thelocations specified by each n-tuple are used to identify an address in a look-up-table. The

contents of this address either use a single bit to indicate whether or not this address was

accessed during training, or store a count of how many times that address occurred.

While the traditional n-tuple classifier deals with binary-valued input vectors, methods

using n-tuple systems with integer-valued inputs have also been developed. Allinson and

Kolcz have developed a method of mapping scalar attributes into bit strings based on a

combination of CMAC and Gray coding methods. This method has the property that for


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small differences in the arithmetic values of the attributes, the hamming distance between

the bit strings is equal to the arithmetic difference. For larger values of the arithmetic

distance, the hamming distance is guaranteed to be above a certain threshold.

The continuous n-tuple method also shares some similarity at the architectural level with

the single layer look-up perceptron of Tattersall et al, though they differ in the way the

class outputs are calculated, and in the training methods used to configure the contents of

the look-up tables (RAMS).

In summary, no existing technique is free from limitations. Further efforts are required to

improve the performances of face recognition techniques, especially in the wide range of

environments encountered in real world.


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2.3. Basic ConceptsProject has been divided into following modules/functions.

2.3.1. PreprocessingAll pictures from the face database (included the subject to be tested) are preprocessed

because the system uses intensity values of the images to train itself and test an image.

The preprocessing is performed in following steps:

2.3.1.1. Gray Scale levelsConverts pictures to 256 gray scale levels because the algorithm operates on intensity

values.

2.3.1.2. Geometric normalizationTaking into account the face annotations using OpenCV Object Detection algorithm for

face, the method resize, scale, rotate and reflect the picture if needed in order to make it

centered and symmetric with relation to the face.

2.3.2. Object DetectionObject detection (face detection particularly in our system) is a basic requirement of face

recognition systems. There are some techniques that extract certain features of the object

of interest from the image and then use heuristics to find combination of those features in

the image. It is hard to extract such features because sometimes object in the image is not

properly placed or there is shadow on half of the object or a half of the object is not

visible due to lighting conditions. Statistical model-based techniques are quite fair.

Statistical model is first trained by providing multiple positive samples and negative

samples and then used to detect objects.

Face detection is a computer technology that determines the locations and sizes of human

faces in digital images. Face tracking is an extension of face-detection when applied to a

video image. Some of the face-detection techniques are Feature-based methods, using

skin color to find face segments, detecting a blinking eye pattern in a video image and

template-based matching. In Feature based technique, the content of a given region of an

image is transformed into features, after which a classifier trained on example faces

decides whether that particular region of the image is a face, or not.

Intels OpenCV (Open Source Computer Vision) library uses such a statistical model to

detect objects. OpenCV provides low-level and high level APIs for face/object detection.


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A low level API allows users to check an individual location within the image by using

the classifier cascade to find whether it contains a face or not. Helper functions calculate

integral images and scale the cascade to a different face size (by scaling the coordinates

of all rectangles of Haar-like features) etc. Alternatively, the higher-level function if

cvDetectObjects does this all. Figure shows the algorithms results

.

2.3.3. Face RecognitionFollowing is the description of the technique used to recognize faces.

2.3.3.1. IntroductionWe used EigenFaces for face recognition. This approach was first developed by Sirovich

and Kirby (1987) and used later by Matthew Turk and Alex Pentland.

A dataset, such as a digital image, consists of a large number of inter-related variables.

Using Principal Component Analysis, the dimensionality of the dataset is reduced while

retaining as much as variation in the dataset as possible. The datasets are transformed to a

new set of uncorrelated variables called the principal components. These principal

components are ordered in such a way that the first few retain most of the variation

present in all of the original variables.

PCA method is applied in face recognition by discriminating the image data into several

classes. There will be a lot of noise in the image caused by differing lighting conditions,

pose and so on. Despite these noises, there are patterns that can be observed in the image

such as the presence of eyes, mouth or nose in a face and the relative distances between

these objects. PCAs aim is to extract these patterns or the features from the image.


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In the domain of face recognition, the principal components (features) are referred to as

Eigen faces. The original image can be reconstructed by summing up all the Eigen faces

in the right proportion by adding more weights to the real features of the face. Certain

Eigen faces that do not contribute to the important face features are omitted. This is

necessary because of performance issues while doing large computations. This idea is

applied in our approach where Eigen faces are prepared from a set of training images and

stored first. Then Eigen face is prepared for the input test image and compare with the

training images. The matching image is the image having similar weights in the test

database.

2.3.3.2. ConceptFace recognition using Eigen faces approach was initially developed by Sirovich and

Kirby and later used by Matthew Turk and Alex Pentland. They showed that a collection

of face images can be approximately reconstructed by storing a small collection of

weights for each face and a small set of standard pictures.

Using Principal Component Analysis on a set of human face images, a set of Eigen faces

can be generated. Eigen faces are a set of eigenvectors used mostly in human face

recognition. Eigenvectors are a set of features that characterize the variation between

face images. These eigenvectors are derived from the covariance matrix of the probability

distribution of the high-dimensional vector space of faces of human beings. The main

idea here is to use only the best Eigen faces that account for the major variance within the

set of face images. By using lesser Eigen faces, computational efficiency and speed is

achieved. The Eigen faces are the basis vectors of the Eigen face decomposition.

Below are the steps of face recognition process:

A training set of same resolution digital images is initially prepared. The images are stored as a matrix with each row corresponding to an image. Each image is represented as a vector with (r X c) elements where r and c are

the number of rows and the number of columns respectively.

An average image is calculated from the individual training set images. For each image, the deviation from the average image is then calculated and

stored.

The Eigen vectors and Eigen values are then calculated. These represent thedirections in which the training set images differ from the average image.


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A new image is then subtracted from the average image and projected into Eigenface space.

This is compared with the projection vectors of training faces and the matchingimage is determined.

A face image is represented by a two dimensional N by N array of intensity values or a

vector of dimension N2. If there is an image of size 128 by 128, then that can be said as a

vector of dimension 16384. Or, this is equivalent to one point in a 16384-dimensional

space. A group of images then maps to a collection of points in this image space. The

training images chosen are all of same dimensions. We need to find the vectors that best

represent the distribution of face images within this image space and these vectors that

define the sub-space of face images are termed as face space. Each vector of length N2

represents an image of dimension N by N and is a linear combination of the original face

images. These vectors are termed as Eigen faces because these are the vectors of the

covariance matrix corresponding to the original face images and they have face-like

appearance.

Flow chart of the Algorithm is shown in Figure.2.1.


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Start training set image

preparation with the same

resolution images

Store image as a matrix represented

as a vector with (r X c) elements

Calculate average image from training

images

Calculate and store deviation of each

image from the average image

Calculate Eigenvectors and Eigenvalues

of the covariance matrix

Calculate covariance matrix

Store the

principalcomponents

End training set

preparation

Test

Input

Image

Subract from the

average image

Project into

Eigenface space

Compare

with

training

vectors

Determine the minimum

Euclidean distance which is

the matching image

End face

recognition

Training set preparation Face recognition stage

Figure 2.1: Flowchart of Face Recognition using Eigen Faces

2.3.3.3. Principal Component Analysis (PCA)It is a way of identifying patterns in data, and expressing the data in such a way as to

highlight their similarities and differences. Since patterns in data can be hard to find in

data of high dimension, where the luxury of graphical representation is not available,

PCA is a powerful tool for analyzing data. The other main advantage of PCA is that once

you have found these patterns in the data, and you compress the data, i.e. by reducing the

number of dimensions, without much loss of information.


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PCA is technic widely used with regard to face recognition. The principle on which it

works is that face pictures have common objects (nose, eyes, mouth, etc). Everypicture

from the face database is treated as pixel vector. The algorithm computes the covariance

matrix with all these vectors and extracts the Eigen vectors and corresponding Eigen

values. Figure illustrates the Eigen value spectrum of face data. PCA algorithm retains

only a few eigenvectors to form a new basis (Eigen Basis).

PCA Training

The training is the common training performed with particular settings. This is the part of

the process and computes the covariance matrix and the Eigen basis. Following are the

PCA steps:

is anN2x1 vector, corresponding to anNxN face imagesI. The idea is to represent (= - mean face) into a low-dimensional space:

mean w1u1 w2u2 . . . wK uK (K


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Step 4: subtract the mean face:

i i

Step 5: compute the covariance matrix C:

Step 6: compute the eigen vectors i of AAT

The matrixAAT is very large ----> not practical!!

Step 6.1: consider the matrixATA(MxMmatrix)

Step 6.2: compute the eigen vectors viof ATA

ATAvi= ivi

What is the relationship between usiand vi?

Thus,AAT andATAhave the same eigenvalues and their eigen vectors are related

as follows: i=Avi

Note 1: AAT can have up toN2eigenvalues and eigenvectors.


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Note 2: ATA can have up toM eigenvalues and eigenvectors.

Note 3: TheM eigenvalues ofATA (along with their corresponding

eigenvectors) correspond to theM largest eigenvalues ofAAT(along with

their corresponding eigenvectors).

Step 6.3: compute theMbest eigenvectors ofAAT: i=Avi

(important:normalize isuch that||i||=1)

Step 7: Keep onlyK eigenvectors (corresponding to theKlargest eigenvalues)

Representing faces into the basis

Each face (minus the mean) in the training set can be represented as a linear

combination of the bestK eigenvectors:

(We call the js Eigen faces)

Each normalized training face iis represented in this basis by a vector:

2.3.3.4. Face Recognition Using EigenfacesGiven an unknown face image (centered and of the same size like the training faces)

follow these steps:

Step 1: normalize :


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Step 2: project on the eigenspace

Step 3: represent as:

Step 4: find er minl||l||

Step 5: if er < Tr, then is recognized as face l from the training set.

The distance eris called distance within the face space (difs)

Comment: we can use the common Euclidean distance to compute er, however, it has

been reported that theMahalanobis distanceperforms better:

(variations along all axes are treated as equally significant)

2.3.3.5. Face Detection Using EigenfacesGiven an unknown image

Step 1: compute

Step 2: compute


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Step 3: compute ed || ||

Step 4: if ed < Td, then is a face.

The distance ed is called distance from face space (dffs)


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Chapter 3 System Analysis

BCIS 28

CHAPTER 3

System Analysis


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3. AnalysisRequirements are capabilities and conditions to which the system and more broadly the

project must conform. A prime challenge of requirements work is to find, communicate and

remember (that usually means record) what is really needed, in a form that clearly speaks to

client and development team members.

3.1. Use Cases Model3.1.1. Use Case DiagramA use case is a sequence of actions that provide a measurable value to an actor. Another way

to look at it is a use case describes a way in which a real-world actor interacts with the

system. In a system use case you include high-level implementation decisions. System use

cases can be written in both an informal manner and a formal manner. Use case Diagrams are

created to visualize the relationship between actors and use cases. A use case diagram is also

used to capture the system functionality as seen by the user.

Figure 3.1: Use Case Diagram


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3.1.2. Use Cases Descriptions in BriefInsoftware engineering,a use case diagram in theUnified Modeling Language (UML) is a

type of behavioral diagram defined by and created from aUse-case analysis.Its purpose is to

present a graphical overview of the functionality provided by a system in terms ofactors,their goals (represented asuse cases), and any dependencies between those use cases.

The main purpose of a use case diagram is to show what system functions are performed for

which actor. Roles of the actors in the system can be depicted.

Use Case diagrams are formally included in two modeling languages defined by theOMG.

Both theUML andSysML standards define a graphical notation for modelinguse cases with

diagrams. One complaint about the standards has been that they do not define a format for

describing these use cases. Generally, both graphical notation and descriptions are important

as they document the use case, showing the purpose for which anactor uses a system.

The use case diagram shows the position or context of the use case among other use cases. As

an organizing mechanism, a set of consistent, coherent use cases promotes a useful picture of

system behavior, a common understanding between the customer/owner/user and the

development team.
http://en.wikipedia.org/wiki/Software_engineeringhttp://en.wikipedia.org/wiki/Unified_Modeling_Languagehttp://en.wikipedia.org/wiki/Use-case_analysishttp://en.wikipedia.org/wiki/Actor_%28UML%29http://en.wikipedia.org/wiki/Use_casehttp://en.wikipedia.org/wiki/Object_Management_Grouphttp://en.wikipedia.org/wiki/Unified_Modeling_Languagehttp://en.wikipedia.org/wiki/Systems_Modeling_Languagehttp://en.wikipedia.org/wiki/Use_caseshttp://en.wikipedia.org/wiki/Actor_%28UML%29http://en.wikipedia.org/wiki/Actor_%28UML%29http://en.wikipedia.org/wiki/Use_caseshttp://en.wikipedia.org/wiki/Systems_Modeling_Languagehttp://en.wikipedia.org/wiki/Unified_Modeling_Languagehttp://en.wikipedia.org/wiki/Object_Management_Grouphttp://en.wikipedia.org/wiki/Use_casehttp://en.wikipedia.org/wiki/Actor_%28UML%29http://en.wikipedia.org/wiki/Use-case_analysishttp://en.wikipedia.org/wiki/Unified_Modeling_Languagehttp://en.wikipedia.org/wiki/Software_engineering


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3.1.3. Use Case Description in DetailFollowing are the cases derived during Analysis.

3.1.3.1. Use Case UC-01: EnrollmentUse Case ID UC-01

Name Enrollment

Primary Actor Operator / Administrator

Secondary Actor User

Stakeholders and

Interests

Operator: Wants accurate and proper face samples of the person i.e.

user.

User: Wants his enrollment into the system.

Preconditions Operator initiated the system. Also camera is attached to thesystem.

Post Conditions Persons face is detected, his accurate face samples captured and

stored and his personal information is also entered.

Main Success

Scenario

1. Operator enters users personal information.2. User sits in front of the camera.3. User is required to stay still and look directly towards the

camera.

4. System detects users face in the snapshot of the user.5. Save the face sample if the face is detected accurately.6. Discard if face is not detected accurately. Repeat steps 4-6

times at least 4 times.

7. Users face samples are stored on the disk.Scenario Extensions 1a.At any time, system fails:

1. Operator restarts System.2a.Camera is attached but it doesntdisplay the video.

1. Make sure device driver is installed2. Make sure any other application is not using the

camera.

3a.Face is not going to be detected.1. Lighting conditions should be sharp2. User is required to stay still and towards the camera.3. There are not two faces in front of the camera.


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3.1.3.2. Use Case UC-02: PreprocessingUse Case ID UC-02

Name Preprocessing

Primary Actor Operator/ Administrator


Stakeholders and

Interests

Operator: face samples of all the enrolled users are preprocessed for

training of face recognition.

Preconditions Operator initiated the system. The face database already contains

the images of all the employees.

Post Conditions All the face samples enrolled are converted to gray scale, aligned

and scaled to be standard size.

Main Success

Scenario

1. System reads the image from the face database.2. Convert it to gray scale.3. Aligns with respect to face.4. Scales to a standard size.5. The image is cropped using the mask or cropped function

such that only the face from fore head to chin and cheek isvisible.

Repeat steps 4-6 for all the images in face data base

6. All the images are preprocessed.Scenario Extensions 1a.At any time, system fails:

To support recovery and ensure that database has not been

damaged.

1. Operator restarts System.2a.Preprocessed images are not accurate.

1. Lighting conditions should be sharp2. Enroll the user again3. User is required to stay still and look directly

towards the camera

4. There are not two faces in front of the camera.


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3.1.3.3. Use Case UC-03: TrainingUse Case ID UC-03

Name Training



Stakeholders and

Interests

Operator: Wants accurate and proper training of the system.

Preconditions Operator initiated the system. The face database already contains

the preprocessed images of all the employees.

Post Conditions All the calculations are performed. PCA is applied to the images,

eigenvectors and eigenvalues are calculated and system is ready for

recognition.

Main Success

Scenario

1. Different images are calculated.2. Preprocessing is applied.3. PCA is applied on the training images.4. Eigenvectors and eigenvalues are generated.5. Euclidean distance is applied.6. System is ready for recognition.

Scenario Extensions 1a.At any time, system fails:To support recovery and ensure that database has not been

damaged.

1.

Operator restarts System.2a.A user is deleted from the database.

1. Delete his/her face samples from the database aswell.

2. Restart training.


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3.1.3.4. Use Case UC-04: TestingUse Case ID UC-04

Name Testing



Stakeholders and

Interests

Operator: Wants accurate and proper training of the system.

User: Wants his recognition by a system.

Preconditions Training is performed, eigenvectors and eigenvalues are generated.

Camera is attached with the computer.

Post Conditions User is recognized and his personal information is returned. If notrecognized, proper message is displayed.

Main Success

Scenario

1. User sits in front of the camera.2. User is required to stay still and look directly towards the

camera.

3. System detects users face inthe snapshot of the user.4. System preprocesses the face sample.5. System starts matching the face sample with the enrolled

ones.

6. If matched, users personal information is returned.7. If not matched, not found message is displayed.8. May repeat steps 1-7 until the user is recognized.

Scenario Extensions 1a.At any time, system fails:To support recovery and ensure that database has not been

damaged.

1. Operator restarts System.2a.A user is deleted from the database.

1. Delete his face samples from the database as well.2.

Restart training.

3a.Camera is attached but it doesnt display the video.1. Make sure device driver is installed.2. Make sure any other application is not using the

camera.

4a.Face is not going to be detected.1. Lighting conditions should be sharp2. User is required to stay still and look directly

towards the camera.

3. There are not two faces in front of the camera.


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3.1.4. System Sequence DiagramsA sequence diagram maps a scenario described by a use case in step by step detail to define

how objects collaborate to achieve your applications goals.

A lifeline in a sequence diagram represents an object and shows all its points of interaction

with other objects in events that are important to it. Lifelines start at the top of a sequence

diagram and descend vertically to indicate the passage of time. Interactions between objects,

messages and replies, are drawn as horizontal direction arrows connecting lifelines. In

addition, boxes known as combine fragments are drawn around sets of arrows to mark

alternative actions, loops, and other control structures.

UML sequence diagrams are typically used to describe object-oriented software systems, they

are also extremely useful as system engineering tools to design system architectures, in

business process engineering as process flow diagrams, as message sequence charts and call

flows for telecom/wireless system design, and for protocol stack design and analysis. The

following are the sequence diagram elements;

Actor

Represents an external person or entity that interacts with the system

Top Package::Actor

Object

Represents an object in the system or one of its components

object

Separator


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Represents an interface or boundary between subsystems, components or units (e.g., air

interface, Internet, network).

Call Message

A call (procedure) message between header elements

Call Message

Return Message

A return message between header elements

Return Message

Free Note

Documentation note that is free-flowing and can be placed anywhere in the diagram

{}

Following are the system sequence diagram of the corresponding use cases.


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3.1.4.1. SSD1: Enrollment

Figure 3.2: SSD1 Enrollment

3.1.4.2. SSD2: Preprocessing

Figure 3.3: SSD2 Preprocessing


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3.1.4.3. SSD3: Training

Figure 3.4: SSD3 Training

3.1.4.4. SSD4: Testing

Figure 3.5: SSD4 Testing


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3.2. Domain / Conceptual ModelA Domain Model is a high-level conceptual model, defining physical and abstract objects in

an area of interest to the Project. It can be used to document relationships between and

responsibilities of conceptual classes (that is, classes that represent the concept of a group ofthings rather than Classes that define a programming object). It is also useful for defining the

terms of a domain.

Figure 3.6: Domain Model


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3.3. Activity DiagramAn activity diagram is a simple and intuitive illustration of what happens in a workflow, what

activities can be done in parallel, and whether there are alternative paths through the

workflow. Activity diagrams as defined in the Unified Modeling Language are derived fromvarious techniques to visually illustrate workflows.

In the Unified Modeling Language, activity diagrams can be used to describe the business

and operational step-by-step workflows of components in a system. n SysML the activity

diagram has been extended to indicate flows among steps that convey physical element (e.g.,

gasoline) or energy (e.g., torque, pressure).

3.3.1. Admin Side Activity Diagram

Figure 3.7: Admin Side Activity Diagram
http://en.wikipedia.org/wiki/Unified_Modeling_Languagehttp://en.wikipedia.org/wiki/Workflowhttp://en.wikipedia.org/wiki/SysMLhttp://en.wikipedia.org/wiki/SysMLhttp://en.wikipedia.org/wiki/Workflowhttp://en.wikipedia.org/wiki/Unified_Modeling_Language


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3.3.2. User Side Activity Diagram

Figure 3.8: User Side Activity Diagram


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Chapter 4 System Design

BCIS 42

CHAPTER 4

System Design


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4. System DesignSoftware design sits at the technical kernel of the software engineering process and is

applied regardless of the development paradigm and area of application. Design is the

first step in the development phase for any engineered product or system. The designers

goal is to produce a model or representation of an entity that will later be built.

Beginning, once system requirement have been specified and analyzed, system design is

the first of the three technical activities -design, code and test that is required to build and

verify software.

The importance can be stated with a single word Quality. Design is the place where

quality is fostered in software development. Design provides us with representations of

software that can assess for quality. Design is the only way that we can accurately

translate a customers view into a finished software product or system. Software design

serves as a foundation for all the software engineering steps that follow. Without a strong

design we risk building an unstable systemone that will be difficult to test, one whose

quality cannot be assessed until the last stage.

During design, progressive refinement of data structure, program structure, and

procedural details are developed reviewed and documented. System design can be viewedfrom either technical or project management perspective. From the technical point of

view, design is comprised of four activities architectural design, data structure design,

interface design and procedural design. A good design must contain the following

characteristics;

In design phase of the software, alternative approaches must be considered basedon the requirements

Design should be traceable to the analysis model Design should exhibit uniformity and integration Design should be structured to accommodate any change Design should be reviewed to minimize errors Ensure the accurate translation of the requirements. It must be readable and understandable. It should provide complete picture of the software. Design forms basis for programming and maintenance


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The Designing of system is an important stage during system development. The following

components are including in system design phase that are related to this project.

4.1. Class DiagramClass or structural diagrams define the basic building blocks of a model. They are used

for static object modeling, describing what attributes and behavior a class has.

Figure 4.1: Class Diagram


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4.2. Entity Relationship DiagramAn entity-relationship model is an abstract and conceptual representation of data. Entity-

relationship modeling is a database modeling method, used to produce a type of

conceptual schema or semantic data model of a system, often a relational database, and itsrequirements in a top-down fashion. Diagrams created by this process are called Entity-

Relationship Diagrams

Figure 4.2: Entity Relationship Diagram


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4.3. Sequence DiagramsA sequence diagram is a kind of interaction diagram that shows how processes operate

with one another and in what order. It is a construct of a Message Sequence Chart. A

sequence diagram shows object interactions arranged in time sequence. It depicts theobjects and classes involved in the scenario and the sequence of messages exchanged

between the objects needed to carry out the functionality of the scenario. Sequence

diagrams are typically associated with use case realizations in the Logical View of the

system under development. Sequence diagrams are sometimes called event diagrams,

event scenarios

A sequence diagram shows, as parallel vertical lines (lifelines), different processes or

objects that live simultaneously, and, as horizontal arrows, the messages exchanged

between them, in the order in which they occur. This allows the specification of simple

runtime scenarios in a graphical manner.

UML sequence diagrams are typically used to describe object-oriented software systems,

they are also extremely useful as system engineering tools to design system architectures,

in business process engineering as process flow diagrams, as message sequence charts

and call flows for telecom/wireless system design, and for protocol stack design and

analysis. The following are the sequence diagram elements;


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4.3.1. SD1: Enrollment

Figure 4.3: Sequence Diagram of Enrollment

4.3.2. SD2: Preprocessing

Figure 4.4: Sequence Diagram of Preprocessing


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4.3.3. SD3: Training

Figure 4.5: Sequence Diagram of Training

4.3.4. SD4: Testing

Figure 4.6: Sequence Diagram of Testing


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Chapter 5 Implementation

BCIS 49

CHAPTER 5

Implementation


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BCIS 50

5. ImplementationThis chapter is about the overview of the technologies used to develop this project.

Implementation is the important phase of software development life cycle where the

thoughts and ideas are given a physical existence. It is just like making your dreams true.

It is the time when software development progress in a full swing manner. An application

is a result of a successful implementation.

Implementation (software) perspective describes software implementations in a particular

technology (such as Java). In the UP, Implementation means programming and building

the system, not deploying it.

During implementation, developers translate the object model in to source code. Thisincludes implementing the attributes and methods of each object and integrating all the

objects such that they function as a single system.

The implementation activity span the gap between the detailed object design model and a

complete set of source code files that can be compiled together.

There are three types of Implementation:

Implementation of a computer system to replace a manual system. The problemsencountered are converting files, training users, and verifying printouts for

integrity.

Implementation of a new computer system to replace an existing one. This isusually a difficult conversion. If not properly planned there can be many

problems.

Implementation of a modified application to replace an existing one using thesame computer. This type of conversion is relatively easy to handle, provided

there are no major changes in the files.

Implementation in Generic tool project is done in all modules. In the first module User

level identification is done. In this module every user is identified whether they are

genuine one or not to access the database and also generates the session for the user.

Illegal use of any form is strictly avoided.


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BCIS 51

5.1. T

system requirement specification

Documents

project report

almighty allah

project wasonly dependent

allah swt

portion of work

thispart of work

islamabadfaculty of

committeeexternal examinermr