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FACE RECOGNITION

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CONTENTS

Introduction History Biometrics Face Authentication Drawbacks Tests and Results Conclusion

Face Recognition

INTRODUCTION

Face recognition is a computer based security system capable of automatically verifying or identifying a person.

Biometrics identifies or verifies a person based on individual’s physical characteristics by matching the real time patterns against the enrolled ones.

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Face Recognition

HISTORY

The first attempts to do this began in the 1960’s with a semi-automated system. The first attempts to do this began in the 1960’s with a semi-automated system.

In 1970’s Goldstein, Harmon and Lesk created a system of 21 subjective markers such as hair color and lip thickness.

In 1988, when Kirby and Sirovich used a standard linear algebra technique, ‘Principle Component analysis’ that reduced the computation to less than a hundred values to code a normalized face image.

In 1991, scientists finally succeeded in developing real time automated face recognition system.

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Face Recognition

BIOMETRICS

Biometric consists of several authentication techniques based on unique physical characteristics such as face, fingerprints, iris, hand geometry, retina, and voice.

Biometric Technologies fill the role of analyzing and measuring unique biological properties in order to produce unique identifications which is then digitalized and stored

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Face Recognition

Biometrics can be divided into two main classes

Physiological biometrics -related to the shape of the body

Behavioral biometrics -related to the behavior of a person.

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Face Recognition

Physiological biometrics is related to the shape of the body

• Face Recognition

A facial recognition technique is an application of computer for automatically identifying or verifying a person from a digital image or a video frame from a video source.

Fig: Recognition of face from Body

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Face Recognition

Facial recognition technologies have recently developed into two areas

Facial metric Eigen faces.

Fig: Eigen Face.

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Face Recognition

• Finger-scan

A fingerprint is an impression of the friction ridges of all or any part of the finger.

A friction ridge is a raised portion of the on the palmar(palm)or digits (fingers and toes) or plantar (sole) skin, consisting of one or more connected ridge units of friction ridge skin.

Fig: Fingerprint Bitmap

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Face Recognition

• Iris-scan This recognition method uses the iris of the

eye which is colored area that surrounds the

pupil. Iris patterns are unique and are obtained

through video based image acquisition system.

Fig: Image of IRIS.

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Face Recognition

• Retina-scan: It is based on the blood vessel pattern in

the retina of the eye as the blood vessels at the back of the eye have a unique pattern, from eye to eye and person to person.

Fig: Image of Retina

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Face Recognition

• Hand-scan: These techniques include the estimation of

length, width, thickness and surface area of the hand.

Various method are used to measure the hands- Mechanical or optical principle.

Fig: Hand Geometry Scanner Fig: Acquired Image of Hand.

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Face Recognition

Behavioral biometrics is related to the behavior of a person.

• Voice-scan: Voice is also physiological trait because

every person has different pitch, but voice recognition is mainly based on the study of the way a person speaks.

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Face Recognition

• Signature-scan:

The signature dynamics recognition is based on the dynamics of making the signature, rather than a direct comparison of the signature itself afterwards.

There are various kinds of devices used to capture the signature dynamics traditional tablets or special purpose devices.

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Face Recognition

Access Control System using Face Recognition

 Face recognition applications are more and more being taken interest in and developed

They are non-intrusive. Biometric data of the faces (photos, videos)

can be easily taken with available devices like cameras.

One biometric data is used in many different environments.

And facial recognition sounds rather interesting in comparison with other biometric technologies.

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Face Recognition

Challenges involved out-of-Plane Rotation: frontal, 45 degree, profile,

upside down Presence of beard, mustache, glasses etc Facial Expressions Occlusions by long hair, hand In-Plane Rotation Image conditions:

Size Lighting condition Distortion Noise Compression

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Face Recognition

Lighting variation Orientation variation (face angle) Size variation Large database Processor intensive Time requirements

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FACE AUTHENTICATION

Face Recognition

MODEL

Access Control System Based on Face Authentication Model

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Image digital

processing

Face Capture devices

Face databas

e

Face detecti

on

ID

ALGORITHMS IN USE

Face Recognition

FACE RECOGNITION MODEL

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Facedetection

Featureextraction

Databaseof enrolled

users

Feature match

Face image

Face id

Face Recognition Processing Flow

Face Recognition

FACE RECOGNITION MODEL CONTD…

Face Detection Feature Extraction Feature Match 

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FACE RECOGNITION ALGORITHM

Face Recognition

GEOMETRIC FEATURE-BASED APPROACH

Based on the geometric characteristics of faces

Parts of human faces such as eyes, nose, and mouth are located together with their attributes

Distinguish faces based on information. This approach is quite effective for small

database, with steady lighting and viewpoint.

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Face Recognition

DISADVANTAGES:

Not effective for unstable lighting condition and changing viewpoint.

The scanning technology is not yet reliable. The information extracted is not enough for

an information-rich organ like face.

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Face Recognition

Geometric feature-based approach

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Face Recognition

APPEARANCE-BASED APPROACH

Based on human appearance.. Transforms the face space into subspaces Fewer dimensions Principal Component Analysis (PCA) KLT – Karhunen- Loève Transform.

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Face Recognition

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EIGENFACE ALGORITHM

Face Recognition

Eigenfaces Initialization1.Acquire an initial set of face images

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2.Calculate the eigen

faces from the training set, keeping only the M images that correspond to the highest eigenvalues. These M images define the face space. As new faces are experienced, the eigen faces can be updated or recalculated

Face Recognition

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3. Calculate the corresponding distribution in M-

dimensional weight space for each known individual, by projecting their face images onto the “face space.”

Face Recogniton

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Face Recognition

EIGENFACES RECOGNITION

1. Calculate a set of weights based on the input image and the M eigenfaces by projecting the input image onto each of the eigenfaces.

2. Determine if the image is a face at all by checking to see if the image is sufficiently close to “face space.”

3. If it is a face, classify the weight pattern as either a known person or as unknown.

4. (Optional) Update the eigenfaces and/or weight patterns.

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Face Recognition

PARAMETER BASED FACIAL RECOGNITION:

Facial image is analyzed and reduced to small set of parameters describing prominent facial features

Major features analyzed are: eyes, nose, mouth and cheekbone curvature

These features are then matched to a database   Advantage: recognition task is not very expensive Disadvantage: the image processing required is

very expensive and parameter selection must be unambiguous to match an individual’s face

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Face Recognition

TEMPLATE BASED FACIAL RECOGNITION

Salient regions of the facial image are extracted

These regions are then compared on a pixel-by-pixel basis with an image in the database

Advantage is that the image preprocessing is simpler

Disadvantage is the database search and comparison is very expensive

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Face Recognition

OTHER APPROACH

Local Features Analysis (LFA) method. Gabor wavelet-based features method. Local Binary Pattern (LBP) method.

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Face Recognition

THREE-DIMENSIONAL FACE RECOGNITION (3D FACE RECOGNITION)

Three-dimensional geometry of the human face is used.

Higher accuracy than their 2D counterparts Multiple images from different angles from a common

camera may be used to create the 3D model with significant post-processing. 

The main technological limitation of 3D face recognition methods is the acquisition of 3D images, which usually requires a range camera.

3D face recognition is still an active research field, though several vendors offer commercial solution.

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Lenovo – Asus – Toshiba

Face Recognition

LENOVO VERIFACE III

User interface of Veriface III, released on Aug 06th 2008. Lenovo has had interesting ads with Robinson and his wife.

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Face Recognition

ASUS SMARTLOGIN

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Face Recognition

 TOSHIBA FACE RECOGNITION

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DRAWBACKS

Face Recognition

The security threat posed to Lenovo’s – Asus’s – Toshiba’s products, based on the basis face recognition algorithms and the tests performed on them:

• Face Recognition in comparison with other biometric recognition systems.

• Influences of varied lighting• Influences of image capturing devices• Influences of Image Processing

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Face Recognition

Table 1: State of art of biometric recognition systems

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Face Recognition

Biometric recognition systemsFinger Print TechnologyFace Recognition TechnologyIris TechnologyHand Geometry TechniqueRetina GeometrySpeaker Recognition Technique (voice)Signature Verification Technique

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BYPASS MODEL

Face Recognition

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FAKE FACE

Face Recognition

How to get a target’s image

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Face Recognition

Fake Face Bruteforce

There are several things to concern about in image editing so as the Brute Force to be successful, including:The image’s viewpoint.Lighting effect.

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TESTS AND RESULTS

Face Recognition

2) Asus SmartLogon V1.0.0005

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Face Recognition

1) Lenovo Veriface III

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Face Recognition

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3) Toshiba Face Recognition 2.0.2.32

Face Recognition

RESULT ESTIMATION

Table 2:results of the tests on the Bypass Model

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CONCLUSION 

•The weak points that might allow one to bypass into the systems of the three big computer manufacturers Lenovo – Asus – Toshiba is to give sufficient evidences that the authentication technologies being used by these three manufacturers are not efficient and secure enough as they are prone to be bypassed putting users’ data at serious risk.

Thank You

Face Recognition

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Questions??