Localized versus Locality-Preserving Subspace Projections for Face Recognition
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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.
KeywordsManifold Image Processing Pattern Recognition Computer Vision Face Recognition
- 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.Google Scholar
- 10.Zhang J, Li SZ, Wang J: Manifold learning and applications in recognition. In Intelligent Multimedia Processing with Soft Computing. Springer, Heidelberg, Germany; 2004.Google Scholar
- 11.FRVT 2002 2004: Evaluation Report, http://www.frvt.org
- 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.Google Scholar
- 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.Google Scholar
- 24.He X, Niyogi P: Locality preserving projections. Proceedings of the Annual Conference on Neural Information Processing Systems 16 (NIPS '03), December 2003, Vancouver, CanadaGoogle Scholar
- 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.Google Scholar
- 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, CanadaGoogle Scholar
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