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A Brief Survey on Face Recognition Systems

A Brief Survey on Face Recognition Systems. Amir Omidvarnia March 2007. Outline. Introduction Face Recognition Concepts 2D Face recognition Systems An Example 3D Face Recognition Suggestions. Introduction. What is face recognition? Applications Security applications

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A Brief Survey on Face Recognition Systems

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  1. A Brief Survey onFace Recognition Systems Amir Omidvarnia March 2007

  2. Outline • Introduction • Face Recognition Concepts • 2D Face recognition Systems • An Example • 3D Face Recognition • Suggestions

  3. Introduction • What is face recognition? • Applications • Security applications • Image search engine

  4. Requirements • Accurate • Efficient • Light invariant • Rotation invariant

  5. Applications of Face Recognition • Desire to locate specific individuals • Criminals • TERRORISTS • Missing Children • Surveillance

  6. Face Recognition Concepts • Enrollment An initial featureset is constructed from the relevant physical traits of the user.

  7. Face Recognition Concepts • Verification Extracted featureset from each person is compared with the enrollment feature set. If the resulting score value is above a predefined threshold, the user is considered to be authenticated.

  8. Face Recognition Concepts • Identification In contrast to the verification use case, with identification the (claimed) identity of the user is not known in advance, but shall be determined based on sample images of the user's face and a set (population) of feature sets with known identities.

  9. An Example:FaceVACS Architecture • Face Localization • Eye Localization • Image Quality Check • Normalization • Preprocessing • Feature Extraction • Construction of the Reference Set • Comparison

  10. Face Recognition Concepts • The Facial Identification Record (FIR) In the result of processing the raw samples (images), e.g. during enrollment, feature sets are created. In the context of FaceVACS-SDK we use the term FIR for these feature sets.

  11. Face Recognition Concepts • Enrollment and Verification

  12. Face Recognition Concepts • FAR, FRR and EER FAR (False Acceptance Rate) is the probability that a sample falsely matches the presented FIR. FRR (False Rejection Rate) is the probability that a sample of the right person is falsely rejected. The value of FAR and FRR at the point where the plots cross is called the Equal Error Rate (EER).

  13. Face Recognition Concepts • FAR, FRR and EER

  14. Face Recognition Systems • Feature-Based • Appearance-Based • Model-Based

  15. Feature-Based Algorithms • Geometric Features • Texture • Skin color • Multiple features

  16. Appearance-Based Algorithms • Eigenface • Fisherface • SVM • Neural Networks • Hidden Markov Models

  17. Model-Based Algorithms • Face Bunch Graph • Predefined face templates • Deformable templates

  18. An Example • Sample Image

  19. An Example • Eye location found by the algorithm

  20. An Example • After Normalization

  21. An Example • After Preprocessing

  22. An Example • Extracting local features

  23. An Example • Forming the reference set of the image

  24. Image Database • Effective Factors in combining FIRs • Influence and arrangement of lighting conditions • Sample Quality • Orientation of Samples • Adornment • Face Angle • Face Appearance

  25. 3D Face Recognition

  26. 3D Face Recognition

  27. Suggestions • A comprehensive Survey on 2D Face Recognition Algorithms • Face Detection • Face Segmentation • Feature Extraction • Facial Models • Texture Analysis • Towards 3D Face Recognition • Combining other Biometrics such as Iris Recognition  Towards Multimodal Systems

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