<ul><li> Zeeshan EJAZ Bhatti 	</li></ul>(Muhammad Ali Jinnah University)<br /><ul><li> Usama IJAZ Bajwa 	</li></ul>(Center...
Agenda of Discussion<br />Introduction <br />Laplacianfaces Algorithm<br />FERET Face Image DataBase<br />csuFaceIdEval<br...
Face Recognition<br />Appearance based face recognition works on the principle of dimensionality reduction.<br />In an m×n...
Face Recognition Algorithms<br />
Introduction<br />Laplacianfaces is an appearance based face recognition algorithm that works on the principal of dimensio...
LaplacianFaces Algorithm<br />Laplacianfaces is claimed to work better than Eigenfaces face recognition algorithm.<br />La...
Laplacianfaces Algorithm<br />13th  IEEE International Multi topic Conference 2009  - Vision and Pattern Recognition Syste...
FERET Face Image Database<br />Face Recognition Technology (FERET) program is managed by the Defense Advanced Research Pro...
Sample Images from FERET<br />13th  IEEE International Multi topic Conference 2009  - Vision and Pattern Recognition Syste...
csuFaceIdEval<br />Originally Developed at Colorado State University.<br />Provides base code for developing and testing F...
Training<br />Training<br />Data<br />Write<br />Has More <br />Images?<br />Image Loading<br />Subspace<br />Training<br ...
Preprocessing<br /><ul><li> Rotation
 Scaling
 Masking
 Equalization</li></ul>Input Image<br />Processed Image<br />13th  IEEE International Multi topic Conference 2009  - Visio...
Projection<br />Training<br />Data<br />Subject<br />Image<br />Read<br />Read<br />Subspace<br />Loading<br />Read Image<...
Experimental Setup<br />We chose a well known algorithm Eigenfaces for the purpose of comparison.<br />Experimentation con...
Varying dimensions of subspace on FAFC (varying illumination)<br />Varying dimensions of subspace on FAFB (varying face ex...
Varying dimensions of subspace on Dup-I (aging of subjects)<br />Varying dimensions of subspace on Dup-II (aging of subjec...
Effect of different distance metrics on FAFC (varying illumination)<br />Effect of different distance metrics on FAFB (var...
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Introducing Set Of Internal Parameters For Laplacian Faces

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Introducing Set Of Internal Parameters For Laplacian Faces

  1. 1. <ul><li> Zeeshan EJAZ Bhatti </li></ul>(Muhammad Ali Jinnah University)<br /><ul><li> Usama IJAZ Bajwa </li></ul>(Center for Advanced Studies in Engineering)<br /><ul><li> Imtiaz AHMED Taj </li></ul>(Muhammad Ali Jinnah University)<br />Introducing Set of Internal Parameters for Laplacian Faces to Enhance Performance under Varying Conditions<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  2. 2. Agenda of Discussion<br />Introduction <br />Laplacianfaces Algorithm<br />FERET Face Image DataBase<br />csuFaceIdEval<br />Experimental Setup<br />Results<br />Conclusion<br />References<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  3. 3. Face Recognition<br />Appearance based face recognition works on the principle of dimensionality reduction.<br />In an m×n image, a pixel can be presented a point (hence a vector) in an m×ndimensional space, called facespace.<br /> A dimensionality reduction technique is employed to reduce the facespace to a subspace.<br />Face recognition problem is hence reduced to a pattern recognition problem in the reduced subspace.<br />Model based face recognition works by taking the geometric information of the facial features.<br />
  4. 4. Face Recognition Algorithms<br />
  5. 5. Introduction<br />Laplacianfaces is an appearance based face recognition algorithm that works on the principal of dimensionality reduction.<br />We evaluated performance of Laplacianfaces in varying lighting condition, facial expressions and aging.<br />Results of Experimentation propose a set of internal parameters to get higher performance.<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  6. 6. LaplacianFaces Algorithm<br />Laplacianfaces is claimed to work better than Eigenfaces face recognition algorithm.<br />Laplacianfaces computes the subspace that preserves the locality information.<br />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.<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  7. 7. Laplacianfaces Algorithm<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />Aside,<br />Objective function of Laplacianfaces algorithm,<br />Where,<br />if xi is among k neighbors of xj<br />or, if xj is among k neighbors of xi ,<br />otherwise,<br />Imposing a constraint on minimization,<br />arg min<br />w<br />The minimization problem is hence transformed to the generalized eigenvector problem.<br />The final transformation matrix,<br />
  8. 8. FERET Face Image Database<br />Face Recognition Technology (FERET) program is managed by the Defense Advanced Research Projects Agency (DARPA) and the National Institute of Standards and Technology (NIST).<br />FERET database contains 14,051 images of 1,201 distinct individuals out of which 3,819 are frontal face image. <br />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.<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  9. 9. Sample Images from FERET<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  10. 10. csuFaceIdEval<br />Originally Developed at Colorado State University.<br />Provides base code for developing and testing Face Recognition algorithms.<br />Four algorithms are already implemented<br />Eigenfaces Algorithm<br />Fisherfaces Algorithm<br />Bayesian Intrapresonal / Extrapersonal<br />Elastic Bunch Graph Matching<br />Provides support for standardized statistical analysis to generate results and reports.<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  11. 11. Training<br />Training<br />Data<br />Write<br />Has More <br />Images?<br />Image Loading<br />Subspace<br />Training<br />Finish<br />No<br />Yes<br />Read Next Image<br />Start<br />Pre-Processing<br />Read<br />Read<br />Image Lists<br />&<br />Images<br />Eye <br />Coordinates<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  12. 12. Preprocessing<br /><ul><li> Rotation
  13. 13. Scaling
  14. 14. Masking
  15. 15. Equalization</li></ul>Input Image<br />Processed Image<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  16. 16. Projection<br />Training<br />Data<br />Subject<br />Image<br />Read<br />Read<br />Subspace<br />Loading<br />Read Image<br />Start<br />Image Loading<br />Finish<br />Subspace<br />Projection<br />Distance<br />Computation<br />Write<br />Distances<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  17. 17. Experimental Setup<br />We chose a well known algorithm Eigenfaces for the purpose of comparison.<br />Experimentation conducted:<br />Varying Facial Expression : FERET fafbprobeset.<br />Varying Illumination : FERET fafcprobeset.<br />Aging of Subjects : FERET dupI and dupIIprobesets.<br />Different number of retained vectors for subspace. <br />50, 100, 150, 200 and 250 retained subspace vectors.<br />Various distance metrics.<br />Euclidean, Cityblock, Covariance, Correlation,<br />LdaSoft, Mahalanobis L1, Mahalanobis L2, Yambor Angle, Yambor Distance.<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  18. 18. Varying dimensions of subspace on FAFC (varying illumination)<br />Varying dimensions of subspace on FAFB (varying face expressions)<br />Results<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  19. 19. Varying dimensions of subspace on Dup-I (aging of subjects)<br />Varying dimensions of subspace on Dup-II (aging of subjects)<br />Results<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  20. 20. Effect of different distance metrics on FAFC (varying illumination)<br />Effect of different distance metrics on FAFB (varying face expressions)<br />Results<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  21. 21. Effect of different distance metrics on Dup-I (aging of subjects)<br />Effect of different distance metrics on Dup-II (aging of subjects)<br />Results<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  22. 22. Conclusion<br />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.<br />Difference in performance of Eigenfaces and Laplacianfaces becomes more significant for higher number of retained vectors for subspace.<br />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.<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />
  23. 23. References<br />Xiaofei He, Shuicheng Yan, YuxiaoHu, ParthaNiyogi, Hong-Jiang Zhang, &quot;Face Recognition Using Laplacianfaces”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, Mar. 2005.<br />M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3 (1), 1991.<br />The Facial Recognition Technology (FERET) Database, NIST, 2001.<br />Image Analysis for Face Recognition, Xiaoguang Lu.<br />The Colorado State University Face Identification Evaluation System, Version 5.0.<br />Modified version of csuFaceIdEval uploaded for public access at http://visprs.com/redir/facerec<br />13th IEEE International Multi topic Conference 2009 - Vision and Pattern Recognition Systems http://visprs.com/<br />

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