Marginal Fisher Regression Classification for Face Recognition
This paper presents a novel marginal Fisher regression classification (MFRC) method by incorporating the ideas of marginal Fisher analysis (MFA) and linear regression classification (LRC). The MFRC aims at minimizing the within-class compactness over the between-class separability to find an optimal embedding matrix for the LRC so that the LRC on that subspace achieves a high discrimination for classification. Specifically, the within-class compactness is measured with the sum of distances between each sample and its neighbors within the same class with the LRC, and the between-class separability is characterized as the sum of distances between margin points and their neighboring points from different classes with the LRC. Therefore, the MFRC embodies the ideas of the LRC, Fisher analysis and manifold learning. Experiments on the FERET, PIE and AR datasets demonstrate the effectiveness of the MFRC.
KeywordsFace recognition Nearest subspace classification Linear regression classification Manifold learning
This work was supported by the National Basic Research Program of China (973 Program) under Grant 2014CB340400, the National Natural Science Foundation of China under Grant 61271325, Grant 61472273, and the Elite scholar Program of Tianjin University under Grant 2015XRG-0014.
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