This presentation was delivered by me in the Computer Vision session of IEEE International Multitopic Conference (INMIC) 2009.

2. Agenda of Discussion Introduction Laplacianfaces Algorithm FERET Face Image DataBase csuFaceIdEval Experimental Setup Results Conclusion References 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

3. Face Recognition Appearance based face recognition works on the principle of dimensionality reduction. In an m×n image, a pixel can be presented a point (hence a vector) in an m×ndimensional space, called facespace. A dimensionality reduction technique is employed to reduce the facespace to a subspace. Face recognition problem is hence reduced to a pattern recognition problem in the reduced subspace. Model based face recognition works by taking the geometric information of the facial features.

4. Face Recognition Algorithms

5. Introduction Laplacianfaces is an appearance based face recognition algorithm that works on the principal of dimensionality reduction. We evaluated performance of Laplacianfaces in varying lighting condition, facial expressions and aging. Results of Experimentation propose a set of internal parameters to get higher performance. 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

6. LaplacianFaces Algorithm Laplacianfaces is claimed to work better than Eigenfaces face recognition algorithm. Laplacianfaces computes the subspace that preserves the locality information. If two faces xi and xj are close to each other in N space, then the respective projected images in K subspace, yi and yj are also close to each other. 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

7. Laplacianfaces Algorithm 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/ Aside, Objective function of Laplacianfaces algorithm, Where, if xi is among k neighbors of xj or, if xj is among k neighbors of xi , otherwise, Imposing a constraint on minimization, arg min w The minimization problem is hence transformed to the generalized eigenvector problem. The final transformation matrix,

8. FERET Face Image Database Face Recognition Technology (FERET) program is managed by the Defense Advanced Research Projects Agency (DARPA) and the National Institute of Standards and Technology (NIST). FERET database contains 14,051 images of 1,201 distinct individuals out of which 3,819 are frontal face image. These pictures are taken in different lighting conditions, face expressions and different days. Each of this variation corresponds to a probe set to evaluate performance of a given algorithm on that specific varying condition. 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

9. Sample Images from FERET 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

10. csuFaceIdEval Originally Developed at Colorado State University. Provides base code for developing and testing Face Recognition algorithms. Four algorithms are already implemented Eigenfaces Algorithm Fisherfaces Algorithm Bayesian Intrapresonal / Extrapersonal Elastic Bunch Graph Matching Provides support for standardized statistical analysis to generate results and reports. 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

11. Training Training Data Write Has More Images? Image Loading Subspace Training Finish No Yes Read Next Image Start Pre-Processing Read Read Image Lists & Images Eye Coordinates 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

13. Scaling

14. Masking

15. EqualizationInput Image Processed Image 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

16. Projection Training Data Subject Image Read Read Subspace Loading Read Image Start Image Loading Finish Subspace Projection Distance Computation Write Distances 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

17. Experimental Setup We chose a well known algorithm Eigenfaces for the purpose of comparison. Experimentation conducted: Varying Facial Expression : FERET fafbprobeset. Varying Illumination : FERET fafcprobeset. Aging of Subjects : FERET dupI and dupIIprobesets. Different number of retained vectors for subspace. 50, 100, 150, 200 and 250 retained subspace vectors. Various distance metrics. Euclidean, Cityblock, Covariance, Correlation, LdaSoft, Mahalanobis L1, Mahalanobis L2, Yambor Angle, Yambor Distance. 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

18. Varying dimensions of subspace on FAFC (varying illumination) Varying dimensions of subspace on FAFB (varying face expressions) Results 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

19. Varying dimensions of subspace on Dup-I (aging of subjects) Varying dimensions of subspace on Dup-II (aging of subjects) Results 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

20. Effect of different distance metrics on FAFC (varying illumination) Effect of different distance metrics on FAFB (varying face expressions) Results 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

21. Effect of different distance metrics on Dup-I (aging of subjects) Effect of different distance metrics on Dup-II (aging of subjects) Results 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

22. Conclusion Laplacianfaces performs better than Eigenfaces for varying illumination and facial expressions. However there is no significant difference of performance between Laplacianfaces and Eigenfaces for the aging of subjects. Difference in performance of Eigenfaces and Laplacianfaces becomes more significant for higher number of retained vectors for subspace. Certain distance metrics may improve performance Laplacianfaces for a specific imaging condition. But generally, the distance metrics that consider the distribution of data, perform better than others. 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/

23. References Xiaofei He, Shuicheng Yan, YuxiaoHu, ParthaNiyogi, Hong-Jiang Zhang, "Face Recognition Using Laplacianfaces”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, Mar. 2005. M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3 (1), 1991. The Facial Recognition Technology (FERET) Database, NIST, 2001. Image Analysis for Face Recognition, Xiaoguang Lu. The Colorado State University Face Identification Evaluation System, Version 5.0. Modified version of csuFaceIdEval uploaded for public access at http://visprs.com/redir/facerec 13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/