Fuzzy MSD based feature extraction method for face recognition

Xiaodong Li

doi:10.1016/j.neucom.2013.06.025

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Fuzzy MSD based feature extraction method for face recognition

2013-12-01

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Xiaodong Li *

Aiguo Song

DOI：10.1016/j.neucom.2013.06.025

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Abstract

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To improve the recognition performance of maximum scatter difference (MSD), a fuzzy MSD method is proposed in this paper. In the existing MSD model, the class mean vector in the expressions of within-class scatter matrix and between-class scatter matrix is estimated by class sample average. Obviously, the class sample average is not sufficient to provide an accurate estimate of the class mean using a few of the given samples, because there will be some outliers in the sample set under the non-ideal conditions such as variations of expression, illumination, pose, and so on. As a result, the recognition performance of traditional MSD model will decrease. To address this problem, inspired by existing fuzzy application, the fuzzy theory is incorporated into traditional maximum scatter difference algorithm. In this method, applying fuzzy K-nearest neighbor (FKNN), the membership degree matrix of training samples is calculated, which is used to get fuzzy means of each class and the average of fuzzy means is then applied to the definition of within-class scatter matrix and between class scatter difference matrix, respectively. The results of experiments conducted on ORL, YALE and FERET face database indicate the effectiveness of the proposed approach. (C) 2013 Elsevier B.V. All rights reserved.

Keywords:

Maximum scatter difference (MSD)

Fuzzy K-nearest neighbor

Face recognition