Abstract
Nonlinear dimensionality reduction and face classifier selection are two key issues of face recognition. In this paper, an efficient face recognition algorithm named Robust Marginal Fisher Analysis (RMFA) is proposed, which uses the recent advances on rank minimization. Marginal Fisher Analysis (MFA) is a supervised manifold learning method who perseveres the local manifold information. However, one major shortcoming of MFA is its brittleness with respect to grossly corrupted or outlying observations. So the main idea of RMFA is as follows. First, the high-dimensional face images are mapped into lower-dimensional discriminating feature space by low-rank matrix recovery (LR), which determines a low-rank data matrix from corrupted input data. Then try to obtain a set of projection axes that maximize the ratio of between-class scatter S b against within-class scatter S w by using MFA. Several experiments are used to illustrate the benefit and robustness of RMFA.
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References
Yang, J., Zhang, D.: Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm Biometrics[C]. IEEE Transaction on Pattern Anal Machine Intell. 29(4), 650–664 (2007)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)
Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)
Belkin, M., Niyogi, P.: Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. In: Advances in Neural Information Processing Systems, vol. 14, pp. 586–691. MIT Press (2001)
Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust Principal Component Analysis. Journal of the ACM (3) (2010)
Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust Principal Component Analysis. Journal of the ACM (3) (2010)
Huang, D., Cabral, R., De la Torre, F.: Robust Regression
Hotelling, H.: Analysis of a complex of statistical variables into principal components. Journal of Educational Psychologh 24(6), 417–441 (1933)
Martinez, A., Benavente, R.: The AR face database. CVC Technical Report, 24 (1998)
Chen, Y.D., Jalali, A., Sanghavi, S., Caramanis, C.: Low-rank Matrix Recovery from Error and Erasures.
Fisher, R.A.: The Statistical Utilization of Multiple Measurements. Annals of Eugenics 8, 376–386 (1938)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press (1990)
Yan, S., Xu, D., Zhang, B., Zhang, H.-J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 40–51 (2007)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. WileyInterscience, Hoboken (2000)
He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.-J.: Face recognition using Laplacianfaces. IEEE Transactions on Pattern analysis and Machine Intelligence 27, 328–340 (2005)
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Yi, S., Chen, C., Cui, J., Ding, Y. (2013). Robust Marginal Fisher Analysis. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_7
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DOI: https://doi.org/10.1007/978-3-319-02961-0_7
Publisher Name: Springer, Cham
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