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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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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