Face recognition using a modified fuzzy linear discriminant analysis method

Authors:
Qianzhou Xu;Xiaobo Lu;Weili Zeng
Affiliations:
School of Automation, Southeast University, Nanjing, China;School of Automation, Southeast University, Nanjing, China;School of Transportation, Southeast University, Nanjing, China
Venue:
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Year:
2012

Citing 4
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Abstract

Linear discriminant analysis (LDA) is a simple but widely used algorithm in the area of face recognition. However, it has some shortcomings in which the relationship of each face to a class is assumed to be crisp. This algorithm was modified by incorporating the membership grade of each face pattern into the calculation of the between-class and within-class scatter matrices, which is known as Fuzzy Fisherface. The Fuzzy Fisherface method introduces a gradual level of assignment of each face pattern to a class by using a membership grading based upon the k-Nearest Neighbor (KNN) algorithm, and it obtains an obviously better performance than the LDA method. However, when computing the fuzzy memberships, only the belong-to information is considered while the not-belong-to information is ignored. In this paper, a further modified fuzzy linear discriminant analysis method is proposed to solve this problem. The experiments were performed on the ORL and FERET face databases, and the results show consistent improvement in the recognition rate.