facial recognition final

8/9/2019 Facial Recognition Final

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Biometrics: Faces and IdentityVerification in a Networked World

CSI7163/ELG5121

Donald [email protected]

Mathew Samuel

[email protected]


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Agenda

Identification Biometrics

Facial Recognition

PCA

3D Expression Invariant Recognition

3D Morphable Model

Biometric Communication

XML implementation of CBEFF Conclusion

Questions


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Biometrics

Refer to a broad range of technologies

Automate the identification or verification of anindividual

Based on human characteristics

Physiological: Face, fingerprint, iris Behavioural: Hand-written signature, gait, voice

Characteristics

011001010010101

011010100100110

001100010010010...

Templates


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Authenticate:

Typical BiometricAuthentication Workflow

Database

Enrollment subsystem

Biometric

reader

Feature

Extractor

Authentication subsystem

Biometric

reader

Feature

Extractor

Biometric

Matcher

Enroll:

Match or

No Match

1010010

Template

1010010

Template


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Identification (1:N)

Biometric

reader

Biometric

Matcher

Identification vs. Verification

Database

Verification (1:1)

Biometric

reader

Biometric

Matcher

ID

Database

This person is

Emily Dawson

Match

I am EmilyDawson


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Facial Recognition

Facial recognition requires 2 steps: Facial Detection (will not present today)

Facial Recognition

Typical Facial Recognition technology

automates the recognition of faces usingone of two 2 modeling approaches:

Face appearance 2D Eigen faces

3D Morphable Model

Face geometry 3D Expression Invariant Recognition


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Facial RecognitionAlgorithms

2D Eigenface Principle Component Analysis (PCA)

3D Face Recognition

3D ExpressionInvariant Recognition

3D Morphable Model


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Facial Recognition: Eigenface

Decompose faceimages into a small

set of characteristic

feature images.

A new face iscompared to these

stored images.

A match is found if

the new faces is closeto one of these

images.


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Facial Recognition: PCA- Overview

Create training set of faces and calculatethe eigenfaces

Project the new image onto the

eigenfaces.

Check if image is close to face space.

Check closeness to one of the known

faces.

Add unknown faces to the training set and

re-calculate


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Facial Recognition: PCATraining Set


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Facial Recognition: PCATraining

Find average oftraining images.

Subtract average face

from each image.

Create covariancematrix

Generate eigenfaces

Each original imagecan be expressed as

a linear combination

of the eigenfaces

face space


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Facial Recognition: PCARecognition

A new image is project into thefacespace.

Create a vector of weights that describes

this image.

The distance from the original image to

this eigenface is compared.

If within certain thresholds then it is a

recognized face.


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Facial Recognition: 3D Expression Invariant

Recognition

Treats face as adeformable object.

3D system maps a

face.

Captures facialgeometry in canonical

form.

Can be compared to

other canonical forms.


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Facial Recognition: 3D Morphable Model

Create a 3D facemodel from 2D

images.

Synthetic facial

images are created toadd to training set.

PCA can then be

done using these

images


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Communication

Common Biometric Exchange FormatsFramework (CBEFF)

XML implementation of CBEFF

CBEFF Data Elements Standard Biometric Header

Biometric Specific Memory Block

Signature or MAC


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Conclusion

Facial scan has unique advantages overother biometrics

Core technologies are highly researched

Automated facial detection and facialrecognition algorithm are not yet mature


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References

Antonini, G. et al. (2003) Independent Component Analysis and Support Vector

Machine forFace Feature Extraction, Signal Processing Institute, Swiss FederalInstitute of Technology Lausanne, Switzerland: 1-8

Bolle, R.M. et al. (2004) Guide to Biometrics, New York: Springer-Verlag: 1-5

Bronstein, A.M. et al. (2003) Expression-Invariant 3D Face Recognition AVBPA,

LNCS (2688): 62-70, Springer-Verlag Berlin Heidelbert

Huang, J et al. (2003) Component-based Face Recognition with 3D Morphable

Models Center forB

iological and Computational Learning, MIT

Jeng, SH. Et al. (1998) Facial Feature Detection Using Geometrical Face Model: An

Efficient Approach Pattern Recognition, vol 31(3): 273-282

Nanavati, S. et al. (2002) Biometrics: Identity Verification in a Networked World, New

York: John Wiley & Sons, Inc: 1-5

Storring, M. (2004) Computer Vision and Human Skin Colour Computer Vision and

Media Technology Laboratory, PHD Dissertation, Aalborg University

Turk, M. (1991) Eigenfaces for Recognition Journal of Cognitive Neuroscience, The

Media Laboratory: Vision and Modeling Group, MIT, vol(3): 1

Vezhevets, V. et al. (2002) A Survey on Pixel-Based Skin Color Detection

Techniques Graphics Medial Laboratory, Faculty fo Computational Mathematics and

Cybernetics, Moscow State University


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Questions


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Facial Detection: Colour

Algorithms: Pixel-based

Region-based

Approaches:

Explicitly defined

region within aspecific colour space

Dynamic skin

distribution model


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Facial Detection: Geometry

Faces decomposeinto 4 main organs

Eyebrows

Eyes

Nose Mouth

Algorithm

Preprocessing

Matching


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Facial Detection: Demo (Torch3Vision)

facial recognition final

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