Biometrics: Faces and Identity Verification in a Networked World

1
Biometrics Faces and Identity Verification in a
Networked World

• CSI7163/ELG5121
• Donald Chow
• dchow082_at_uottawa.ca
• Mathew Samuel
• msamuel_at_site.uottawa.ca

2
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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Three Basic Identification Methods
Sidova 750426
Possession (something I have)
Knowledge (something I know)

• Universal
• Unique
• Permanent
• Collectable
• Acceptance

• Universal
• Unique
• Permanent
• Collectable
• Acceptance

• Password
• PIN

• Keys
• Passport
• Smart Card

ü
ü
ü
ü
Biometrics (something I am)

• Universal
• Unique
• Permanent
• Collectable
• Acceptance

ü

• Face
• Fingerprint
• Iris

ü
ü
ü
?
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Biometrics

• Refer to a broad range of technologies
• Automate the identification or verification of an
  individual
• Based on human characteristics
• Physiological Face, fingerprint, iris
• Behavioural Hand-written signature, gait, voice

Templates
Characteristics
011001010010101 011010100100110 001100010010010.
..
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Typical Biometric Authentication Workflow
Database
Match or No Match
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Identification vs. Verification
Identification (1N)
Database
Biometric reader
Biometric Matcher
This person is Emily Dawson
I am Emily Dawson
Verification (11)
ID
Biometric reader
Biometric Matcher
Match
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Faces

• Faces are integral to human interaction
• Manual facial recognition is already used in
  everyday authentication applications
• ID Card systems (passports, health card, and
  drivers license)
• Booking stations
• Surveillance operations

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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 using one of two 2
  modeling approaches
• Face appearance
• 2D Eigen faces
• 3D Morphable Model
• Face geometry
• 3D Expression Invariant Recognition

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Facial Recognition Algorithms

• 2D Eigenface
• Principle Component Analysis (PCA)
• 3D Face Recognition
• 3D Expression Invariant Recognition
• 3D Morphable Model

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Facial Recognition Eigenface

• Decompose face images into a small set of
  characteristic feature images.
• A new face is compared to these stored images.
• A match is found if the new faces is close to one
  of these images.

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Facial Recognition PCA - Overview

• Create training set of faces and calculate the
  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 PCA Training Set
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Facial Recognition PCA Training

• Find average of training images.
• Subtract average face from each image.
• Create covariance matrix
• Generate eigenfaces
• Each original image can be expressed as a linear
  combination of the eigenfaces face space

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Facial Recognition PCA Recognition

• A new image is project into the facespace.
• 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 a deformable object.
• 3D system maps a face.
• Captures facial geometry in canonical form.
• Can be compared to other canonical forms.

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Facial Recognition 3D Morphable Model

• Create a 3D face model from 2D images.
• Synthetic facial images are created to add to
  training set.
• PCA can then be done using these images

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Pros and Cons

• 2D face recognition methods are sensitive to
  lighting, head orientations, facial expressions
  and makeup.
• 2D images contain limited information
• 3D Representation of face is less susceptible to
  isometric deformations (expression changes).
• 3D approach overcomes problem of large facial
  orientation changes

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Communication

• Common Biometric Exchange Formats Framework
  (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 over other
  biometrics
• Core technologies are highly researched
• Automated facial detection and facial recognition
  algorithm are not yet mature

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References

• Antonini, G. et al. (2003) Independent Component
  Analysis and Support Vector Machine for Face
  Feature Extraction, Signal Processing Institute,
  Swiss Federal Institute 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-Invarian
  t 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 for
  Biological 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 a specific
  colour space
• Dynamic skin distribution model

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Facial Detection Geometry

• Faces decompose into 4 main organs
• Eyebrows
• Eyes
• Nose
• Mouth
• Algorithm
• Preprocessing
• Matching

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Facial Detection Demo (Torch3Vision)