face recognition face identification

Click here to load reader

Post on 23-Jan-2017

638 views

Category:

Engineering

11 download

Embed Size (px)

TRANSCRIPT

  • PROPOSED WORK ONFACE RECOGNITION

    Presented By

    Kalyan Acharjya

    A Presentation on Initial stage of M.Tech Dissertation Work

    Department ofUniversity of..

  • 2

    There are some images (slide No 8) used within this presentation were

    copied from internet without prior permission from original owner.

    Only Original Owner has full rights reserved for copied images.

    This PPT is only for fair academic use (Not Commercial).

    Kalyan Acharjya

    kalyan.acharjya@gmail.com

    www.factsaboutuniversity.com

    Disclosure

  • CONTENTS

    Introduction to Digital Image Processing.

    Face Recognition.

    Why Face Recognition.

    How Face Recognition.

    Literature Survey [Going On].

    Problem Statement.

    Challenges for Real Time Applications.

    Standard Face Images Database.

    Conclusions and Future Work.

    References.

    3

    Kalyan Acharjya, India

  • INTRODUCTION : DIGITAL IMAGE PROCESSING

    An Image has infinite intensity value.

    Also infinite picture point -How its stored?

    Digitization of image.

    Spatial discretization by Sampling.

    Intensity discretization by Quantization.

    An stored image is process in various means

    (Techniques) for enhance or extracts some

    features from it, is well considerable as

    DIGITAL IMAGE PROCESSING. 4

    Kalyan Acharjya, India

  • FACE RECOGNITION

    How computer or systems is identify any person by comparison its

    FACE with its previous stored database. Its also a part of

    COMPUTER VISION.

    FACE RECOGNITION is the part of Digital Image Measurement.

    Its High Level Processing involved making sense of an ensemble of

    recognize FACE with analysis of unknown FACE.

    Its almost similar to Human being, who identify any person, if

    he/she have already met. Although it(Human) fails sometimes (rare

    case).5

    Kalyan Acharjya, India

  • WHY FACE RECOGNITION ?

    The world is urged for more and accurate face recognition rate.

    How COMPUTER VISITON is possible, as human being are?

    Automatic person identification.

    FACE RECOGNISITION have lots of real world applications.

    Automatic Attendance System.

    Security Purposes.

    Computer Interaction etc.

    Crowd Surveillance. [US (MIT) invested $ 100 million for perfect recognition

    system-Times of India, Oct 2013 ].

    In 2011, London riots many suspects of partial face images were not

    recognized by COTS FR system[15].6

    Kalyan Acharjya, India

  • Comparison

    HOW FACE RECOGNITION?

    7

    Input Image

    Face Detection*

    Crop Face Image

    Features Extraction

    Identification

    Face Image

    Database

    *The targeted work will not include Face Detection Part.*The input images will crop face images from standard Face Database.

    Who is She?

    Kalyan Acharjya, India

  • LITERATURE SURVEY [1]

    Title : Li, Liao and Jain, Partial Face Recognition,Alignment free Approach, IEEE, May, 2013.

    Technique Used: Authors proposed an alignment freeface recognition method based on multi-key pointdescriptors. (MKD).

    Conclusion: Authors concluded that MKD method issuperior than leading commercial FR systems likePitpatt and faceVACS SDK.

    8

    Kalyan Acharjya, India

  • 9

    LITERATURE SURVEY CONTD..[2]

    Title : Mersico, Nappi and Wechsler, Robust FaceRecognition fro Uncontrolled Pose and Illumination ,IEEE, January, 2013.

    Technique Used: Authors proposed a novel frame workbased on normalization strategies and Face Analysisfor Commercial Entities (FACE).

    Conclusion: The result showed the significant increasein recognition rate [95% in FERET fa Database] inaccuracy, whether comparison with other availablealgorithms.

    Kalyan Acharjya, India

  • 10

    LITERATURE SURVEY CONTD.. [3]

    Title : Park and Savvides , Individual Kernal TensorSubspaces for Robust Face Recognition: A ComputationallyEfficient Tensor Framework without requiring ModeFactorization , IEEE, Oct, 2007.

    Technique Used: The work based on high order tensor toconstruct a multi linear structure and model the multiplefactors of face variations.

    Conclusion: The paper introduced the new concept thatappearance factor, the factor of persons identity modeledby a tensor structure can be used for better facerecognition system specially for difference types ofappearance of same faces.

    Kalyan Acharjya, India

  • 11

    LITERATURE SURVEY CONTD.. [4]

    Title: Karim, Lipu, Rahman and Sultana , Face Recognitionusing PCA based Method, IEEE, 2010.

    Technique Used: The work based on Principle ComponentAnalysis (PCA).

    Conclusion: The paper concluded the Principle ComponentAnalysis is better then their predecessor, where recognitionrate 84.1 % (Male Face) and 95.45 % (Female Face) in caseof Indian face database.

    Also recognition rate 92.5 % (Male Face) and 85 % (FemaleFace) in case of University of Essex, UK face database.

    Kalyan Acharjya, India

  • 12

    LITERATURE SURVEY CONTD..[5] MANY MORE..!

    Title : Meade, Kumar and Phillips, Comparativeperformance of Principle Component Analysis , GaborWavelets and Discrete Gabor Wavelets, CanadianJournal of Electronics and Computer Engg., Spring,2005.

    Technique Used: Comparative performance analysis ofPCA with Gabor Wavelets and Discrete GaborWavelets.

    Conclusion: Gabor Wavelets showed the bestperformance on FERET database, as Gabor Wavelets isleast affected by illumination levels.

    Kalyan Acharjya, India

  • 13

    PROBLEM STATEMENT

    Maximum works were proposed the Face Recognition system in

    particular case (Either Pose or Illumination or specific Face

    Database).

    Till date there is not single face recognition system for fulfilling

    the all (or Maximum factors) in real time application.

    Every method have its pros and cons.

    The presenter motivated by the unsatisfactory scenario of Face

    Recognition system to enhance the performance of it.

    Kalyan Acharjya, India

  • 14

    AVAILABLE FACE RECOGNITION ALGORITHMSBASED ON [4]

    Principle Component Analysis (PCA).

    Normalization of Histogram Analysis (NHA).

    Independent Component Analysis (ICA).

    Normalized Cross Correlation (NCC).

    Sum of Absolute Difference (SAD).

    Linear Discernment Analysis (LDA).

    Discrete Wavelets Transform (DWT).

    Gabor Wavelet Transform(GWT).

    Multilayer Appearance-Tensor based (MAT).

    Multiple Descriptor Key point (MDK-SDK).(Partial Face Also) etc.

    Kalyan Acharjya, India

  • 15

    CHALLENGE FOR FACE RECOGNITION [1]

    External OcclusionBy other face

    Self OcclusionBy non frontal pose

    Facial AccessoriesBy Sunglass

    Not Proper Illumination

    Sensor saturationBy under exposure orover exposure

    Limited Field of View (FOV) By out of camera.

    Kalyan Acharjya, India

  • STANDARD FACE RECOGNITION DATABASE [4]

    The Choice of appropriate database to be used based

    on targeted work.

    Color FERET Database

    Yale Face Database.

    PIE Database.

    FIA Video Database.

    CBCL Face Recognition Database.

    Expression Image Database.

    Mugs hot Identification Database.

    Identification Database.

    Indian Face Database.

    Face Recognition Data, University of Essex, UK 16

    Kalyan Acharjya, India

  • 17

    CONCLUSIONS AND FUTURE WORK

    The presenter in initial phase of project work, so exact algorithms

    yet to be finalized.

    The presenter aware to follow any based paper should have same

    face database.

    Modify the any previous mentioned face recognition algorithms to

    enhance the recognition rate of identification.

    The proposed work may also target for fusion between two

    algorithms.

    When available algorithms will modified or develop, the result will

    be compare with based paper or predecessor method result .

    Kalyan Acharjya, India

  • 18

    REFERENCES

    [1] Li, Liao and Jain, Partial Face Recognition, Alignment free Approach, IEEE

    Transaction on Pattern Analysis and Machine Intelligence, VOL 35, No 5, May,

    2013.

    [2] Mersico, Nappi and Wechsler, Robust Face Recognition fro Uncontrolled Pose

    and Illumination , IEEE Transaction on Systems, man and Cybernetics:

    systems, VOL. 43, NO. 1, January, 2013.

    [3] Park and Savvides , Individual Kernal Tensor Subspaces for Robust Face

    Recognition: A Computationally Efficient Tensor Framework without requiring

    Mode Factorization , IEEE on Systems, man and Cybernetics: systems, VOL. 37,

    NO. 5 , Oct, 2007.

    [4] Karim, Lipu, Rahman and Sultana ,Face Recognition using PCA based

    Method, IEEE, 2010.

    [5] Meade, Kumar and Phillips, Comparative performance of Principle

    Component Analysis , Gabor Wavelets and Discrete Gabor Wavelets, Canadian

    Journal of Electronics and Computer Engg., VOL. 30, NO. 2, Spring, 2005.

    Kalyan Acharjya, India

  • REFERENCES CONTD..

    [6]Kar, Debbarma, Saha and Pal, "Study of Implementing AutomatedAttendance System using Face Recognition Technique International Journal ofComputer and Communication Engineering, VOL. 1, No. 2, July 2012.

    [7] Balcoh, Yousaf, Waqar and Baig,Algorithm for Efficient AttendanceManagement: Face Recognition based Approach, IJCSI (Online), Vol.9, No.1,July 2012.

    [8] Jiang, Sadka and Crooks, "Technical Correspondence-Multimodal BiometricHuman Recognition for Perceptual Human-Computer Interaction IEEETran

View more