Robust regression based face recognition with fast outlier removal
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In this paper, we propose a new robust face recognition method through pixel selection. The method is based on the subspace assumption that a face can be represented by a linear combination in terms of the samples from the same subject. In order to obtain a reliable representation, only a subset of pixels with respect to smallest residuals are taken into the estimation. Outlying pixels which deviate from the linear model of the majority are removed using a robust estimation technique — least trimmed squares regression (LTS). By this method, the representation residual with each class is computed from only the clean data, which gives a more discriminant classification rule. The proposed algorithm provides a novel way to tackle the crucial occlusion problem in face recognition. Evaluation of the proposed algorithm is conducted on several public databases for the cases of both artificial and nature occlusions. The promising results show its efficacy.
KeywordsFace recognition Robust regression Least trimmed sum of squares
Wankou Yang was supported by NSFC under project No.61375001.
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