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Microsoft Research March 17, 2006 Face recognition: Opportunities and Challenges Yu Hen Hu University of Wisconsin – Madison Dept. Electrical and Computer Enginerring Madison, WI 53706 yhhu@wisc.edu Collaborators: Nigel Boston, Charles Dyer, Weiyang Lin, Ryan Wong, Goudong Guo Agenda

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Face recognition: Opportunities and Challenges


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    1. Microsoft Research March 17, 2006 Face recognition: Opportunities and Challenges Yu Hen Hu University of Wisconsin – Madison Dept. Electrical and Computer Enginerring Madison, WI 53706 yhhu@wisc.edu Collaborators: Nigel Boston, Charles Dyer, Weiyang Lin, Ryan Wong, Goudong Guo

    2. Agenda • Human Face Recognition Problem • Recent Progress and Findings • Research at UW-Madison • Invariant of transformation group • Existing Invariants • Integral invariant • Summation invariant • Experiment Environment • Face Recognition Grand Challenge • BEE: Biometric Experimentation Environment • Preliminary FRGC Results (c) 2004-2006 by Yu Hen Hu

    3. Face Recognition Problem gallery probe image Probe (c) 2004-2006 by Yu Hen Hu

    4. Face Recognition by Human A. Adler and J. Maclean, “Performance comparison of human and automatic face recognition” Biometrics Consortium Conference 2004 (c) 2004-2006 by Yu Hen Hu

    5. Understanding Human Face Recognition • Many physiological and psychological studies have been conducted. Eg. Pawan Sinha @MIT. For example, we learned • facial configuration plays an important role in human judgments of identity [Sinha05] Celebrity faces created using identikits [Sinha05] C:\users\yuhen\1Research\face\sinha\ (c) 2004-2006 by Yu Hen Hu

    6. Human Face Recognition Limitations Harmon and Julesz, 1973 Degraded images Human can handle low resolution celebrity images quite well. [Sinha05] (c) 2004-2006 by Yu Hen Hu

    7. Face Recognition by Machine Pre-processing Feature Extraction Pattern Classification (c) 2004-2006 by Yu Hen Hu

    8. Face Recognition Process • A pattern classification problem. • Two steps: • Feature extraction • Classification • Feature extraction properties • Invariant • Discriminant • Classification • Often use distance based method Query image Feature Extraction Query image feature vector Gallery face images feature vectors Pattern Classification Recognition results Wei-Yang Lin (c) 2004-2006 by Yu Hen Hu

    9. Challenges • Sinha et al [2005] use this example to illustrate the difficulty of finding a suitable “similarity” measure to gauge similarity between a pair of faces. • In this example, the outer two faces actually belong to the same person while the middle one does not. But conventional pixel-based measures who say otherwise. • Common variations in pose (this case), lighting, expression, distance, aging remain challenges to face recognition. (c) 2004-2006 by Yu Hen Hu

    10. Face Recognition Challenges Table 1 Face Recognition Technology Evaluation Size Note that the MCINT portion of FRVT 2002 is the only test in this chart that included “video” signatures.Signatures in all other tests were a single still image. http://www.frvt.org/FRVT2002/default.htm (c) 2004-2006 by Yu Hen Hu

    11. Face Recognition Grand Challenge • Goal: to advance performance of face recognition by 10-fold (20%  2% verification rate @0.1% false alarm rate) • Focus on five different scenarios. • Status: on-going to be concluded by the end of 2005 (c) 2004-2006 by Yu Hen Hu

    12. Object Recognition • Three Approaches for object recognition (Powen Sinha) • Transformationist approach • Requires normalization • Computationally expensive. • View-based approach • Store all possible views of the same object • Expensive on storage. • Invariant-based approach • Different views  same invariant features • Desired properties of features • Invariant to variations of the same object • Discriminate to separate similar objects (c) 2004-2006 by Yu Hen Hu

    13. Euclidean Similarity Affine Projective Geometric Transformation Groups (c) 2004-2006 by Yu Hen Hu

    14. Moment Invariants • Introduced by M. K. Hu in 1962 • Advantages • Do NOT require parameterization. • Not sensitive to noise. • Limitations • Low discriminating power. • Local characteristics can NOT be extracted. (c) 2004-2006 by Yu Hen Hu

    15. Differential Invariants • Two examples in 2D • Limitation • sensitive to noise (c) 2004-2006 by Yu Hen Hu

    16. Integral Invariants • Hann and Hickman [2002] extend transformation to integrals • Advantages • do NOT require derivatives • local characteristics can be extracted • Invariants can be systematically generated • Limitation • Require analytical expression of shape (c) 2004-2006 by Yu Hen Hu

    17. Summation Invariants • Introduced by Lin et al. [2005] • Advantages • systematical approach • robustness • high discriminating power • Limitation • require parameterization (c) 2004-2006 by Yu Hen Hu

    18. Method of Moving Frame • The method of moving frame, introduce by Elie Cartan, is a tool for finding invariants under group actions. • Definition: A moving frame is a smooth, G-equivariant map (c) 2004-2006 by Yu Hen Hu

    19. Example: Differential Invariants of E(2) (c) 2004-2006 by Yu Hen Hu

    20. Example: Integral Invariants of E(2) (c) 2004-2006 by Yu Hen Hu

    21. Example: Summation Invariants of E(2) (c) 2004-2006 by Yu Hen Hu

    22. Euclidean Summation Invariants of Curves • Given a curve under Euclidean transformation • We can find a moving frame by solving (c) 2004-2006 by Yu Hen Hu

    23. Euclidean Summation Invariants of Curves • From the moving frame, a family of invariant functions can be derived (c) 2004-2006 by Yu Hen Hu

    24. Euclidean Summation Invariants of Curves • The first summation invariants are explicitly shown below where (c) 2004-2006 by Yu Hen Hu

    25. Euclidean Summation Invariants of Surfaces • Similarly, the family of surface invariants where (c) 2004-2006 by Yu Hen Hu

    26. Face Recognition Grand Challenge (FRGC) • Organized by NIST to facilitate the development of FR technology • Provide challenging problems and facial images • FRGC v2.0 dataset contains 50,000 recordings, including • high resolution still images • 3D images (c) 2004-2006 by Yu Hen Hu

    27. Four FRGC Experiments • Controlled indoor • Multiple images • 3D images • Controlled vs. uncontrolled (c) 2004-2006 by Yu Hen Hu

    28. Baseline @ FAR = 0.1% (c) 2004-2006 by Yu Hen Hu

    29. Biometric Experimentation Environment (BEE) Image Preprocessing BioBox Sub-similarity Generation Similarity Normalization Analysis (c) 2004-2006 by Yu Hen Hu

    30. Sub-similarity Generation (c) 2004-2006 by Yu Hen Hu

    31. FRGC 3D Experiment (c) 2004-2006 by Yu Hen Hu

    32. FRGC 3D Baseline Algorithm (c) 2004-2006 by Yu Hen Hu

    33. Proposed Algorithm shape curve SI PCA similarity score similarity score + PCA similarity score shape surface SI (c) 2004-2006 by Yu Hen Hu

    34. Experiment Setup • Use only 3D shape. Note 2D texture is not utilized in our experiment. • Specified the region of interest • At each pixel, compute summation invariants from a specified window region • Perform PCA to reduce dimensionality (c) 2004-2006 by Yu Hen Hu

    35. 3D Facial Surface and its Summation Invariants (c) 2004-2006 by Yu Hen Hu

    36. 3D Facial Surface and its Summation Invariants (c) 2004-2006 by Yu Hen Hu

    37. ROC Performance of Curve Invariants (c) 2004-2006 by Yu Hen Hu

    38. ROC Performance of Surface Invariants (c) 2004-2006 by Yu Hen Hu

    39. Fusion of Summation Invariants (c) 2004-2006 by Yu Hen Hu

    40. Comparison with Baseline Algorithm (c) 2004-2006 by Yu Hen Hu

    41. Conclusion • Summation invariants • novel geometric feature • provide useful shape information • fusion further improve recognition performance • In principle, one can apply summation invariants to unnormalized images (c) 2004-2006 by Yu Hen Hu