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Localized versus Locality-Preserving Subspace Projections for Face Recognition

Abstract

Three different localized representation methods and a manifold learning approach to face recognition are compared in terms of recognition accuracy. The techniques under investigation are (a) local nonnegative matrix factorization (LNMF); (b) independent component analysis (ICA); (c) NMF with sparse constraints (NMFsc); (d) locality-preserving projections (Laplacian faces). A systematic comparative analysis is conducted in terms of distance metric used, number of selected features, and sources of variability on AR and Olivetti face databases. Results indicate that the relative ranking of the methods is highly task-dependent, and the performances vary significantly upon the distance metric used.

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References

  1. 1.

    Kong SG, Heo J, Abidi BR, Paik J, Abidi MA: Recent advances in visual and infrared face recognition—a review. Computer Vision and Image Understanding 2005,97(1):103-135. 10.1016/j.cviu.2004.04.001

  2. 2.

    Zhao W, Chellappa R, Phillips PJ, Rosenfeld A: Face recognition: a literature survey. ACM Computing Surveys 2003,35(4):399-458. 10.1145/954339.954342

  3. 3.

    Lu J, Plataniotis KN, Venetsanopoulos AN: Face recognition using kernel direct discriminant analysis algorithms. IEEE Transactions on Neural Networks 2003,14(1):117-126. 10.1109/TNN.2002.806629

  4. 4.

    Yang M-H: Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods. Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition (FGR '02), May 2002, Washington, DC, USA 215-220.

  5. 5.

    Yang J, Frangi AF, Yang J-Y, Zhang D, Jin Z: KPCA plus LDA: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005,27(2):230-244.

  6. 6.

    Yang J, Gao X, Zhang D, Yang J-Y: Kernel ICA: an alternative formulation and its application to face recognition. Pattern Recognition 2005,38(10):1784-1787. 10.1016/j.patcog.2005.01.023

  7. 7.

    Heisele B, Ho P, Wu J, Poggio T: Face recognition: component-based versus global approaches. Computer Vision and Image Understanding 2003,91(1-2):6-21. 10.1016/S1077-3142(03)00073-0

  8. 8.

    Lucey S, Chen T: A GMM parts based face representation for improved verification through relevance adaptation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), June-July 2004, Washington, DC, USA 2: 855-861.

  9. 9.

    He X, Yan S, Hu Y, Niyogi P, Zhang H-J: Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005,27(3):328-340.

  10. 10.

    Zhang J, Li SZ, Wang J: Manifold learning and applications in recognition. In Intelligent Multimedia Processing with Soft Computing. Springer, Heidelberg, Germany; 2004.

  11. 11.

    FRVT 2002 2004: Evaluation Report, http://www.frvt.org

  12. 12.

    Li SZ, Hou XW, Zhang HJ, Cheng QS: Learning spatially localized, parts-based representation. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 1: 207-212.

  13. 13.

    Bartlett MS, Movellan JR, Sejnowski TJ: Face recognition by independent component analysis. IEEE Transactions on Neural Networks 2002,13(6):1450-1464. 10.1109/TNN.2002.804287

  14. 14.

    Hoyer PO: Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research 2004, 5: 1457-1469.

  15. 15.

    Lee DD, Seung HS: Learning the parts of objects by non-negative matrix factorization. Nature 1999,401(6755):788-791. 10.1038/44565

  16. 16.

    Paatero P, Tapper U: Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 1994,5(2):111-126. 10.1002/env.3170050203

  17. 17.

    Barlow HB: Unsupervised learning. Neural Computation 1989,1(3):295-311. 10.1162/neco.1989.1.3.295

  18. 18.

    Bell AJ, Sejnowski TJ: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 1995,7(6):1129-1159. 10.1162/neco.1995.7.6.1129

  19. 19.

    Draper BA, Baek K, Bartlett MS, Beveridge JR: Recognizing faces with PCA and ICA. Computer Vision and Image Understanding 2003,91(1-2):115-137. 10.1016/S1077-3142(03)00077-8

  20. 20.

    Tenenbaum JB, de Silva V, Langford JC: A global geometric framework for nonlinear dimensionality reduction. Science 2000,290(5500):2319-2323. 10.1126/science.290.5500.2319

  21. 21.

    Roweis ST, Saul LK: Nonlinear dimensionality reduction by locally linear embedding. Science 2000,290(5500):2323-2326. 10.1126/science.290.5500.2323

  22. 22.

    Belkin M, Niyogi P: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 2003,15(6):1373-1396. 10.1162/089976603321780317

  23. 23.

    Bengio Y, Paiement J-F, Vincent P, Delalleau O, Le Roux N, Ouimet M: Out-of-sample extensions for LLE, isomap, MDS, eigenmaps, and spectral clustering. Proceedings of the Annual Conference on Neural Information Processing Systems 16 (NIPS '03), December 2003, Vancouver, Canada 177-184.

  24. 24.

    He X, Niyogi P: Locality preserving projections. Proceedings of the Annual Conference on Neural Information Processing Systems 16 (NIPS '03), December 2003, Vancouver, Canada

  25. 25.

    Kokiopoulou E, Saad Y: Face recognition using OPRA -faces. Proceedings of the 4th International Conference on Machine Learning and Applications (ICMLA '05), December 2005, Los Angeles, Calif, USA 2005: 69-74.

  26. 26.

    Guillamet D, Vitrià J: Classifying faces with non-negative matrix factorization. Proceedings of the 5th Catalan Conference on Artificial Intelligence (CCIA '02), 2002, Castelló de la Plana, Spain 2504: 24-31.

  27. 27.

    Penev PS, Atick JJ: Local feature analysis: a general statistical theory for object representation. Network: Computation in Neural Systems 1996,7(3):477-500. 10.1088/0954-898X/7/3/002

  28. 28.

    Moghaddam B, Pentland A: Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997,19(7):696-710. 10.1109/34.598227

  29. 29.

    Bowyer KW, Phillips PJ: Empirical Evaluation Techniques in Computer Vision. Wiley-IEEE Computer Society Press, Hoboken, NJ, USA; 1998.

  30. 30.

    Donoho D, Stodden V: When does non-negative matrix factorization give a correct decomposition into parts? Proceedings of the Annual Conference onNeural Information Processing Systems 16 (NIPS '03), December 2003, Vancouver, Canada

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Correspondence to IulianB Ciocoiu.

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Keywords

  • Manifold
  • Image Processing
  • Pattern Recognition
  • Computer Vision
  • Face Recognition