Face recognition using a fuzzy fisherface classifier

  • Authors:

  • Keun-Chang Kwak;Witold Pedrycz

  • Affiliations:

  • Department of Electrical Engineering, Chungbuk National University, Cheongju 361-763, Korea and Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada T6G 2G7;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada T6G 2G7 and Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland

  • Venue:
  • Pattern Recognition
  • Year:
  • 2005

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Abstract

In this study, we are concerned with face recognition using fuzzy fisherface approach and its fuzzy set based augmentation. The well-known fisherface method is relatively insensitive to substantial variations in light direction, face pose, and facial expression. This is accomplished by using both principal component analysis and Fisher's linear discriminant analysis. What makes most of the methods of face recognition (including the fisherface approach) similar is an assumption about the same level of typicality (relevance) of each face to the corresponding class (category). We propose to incorporate a gradual level of assignment to class being regarded as a membership grade with anticipation that such discrimination helps improve classification results. More specifically, when operating on feature vectors resulting from the PCA transformation we complete a Fuzzy K-nearest neighbor class assignment that produces the corresponding degrees of class membership. The comprehensive experiments completed on ORL, Yale, and CNU (Chungbuk National University) face databases show improved classification rates and reduced sensitivity to variations between face images caused by changes in illumination and viewing directions. The performance is compared vis-a-vis other commonly used methods, such as eigenface and fisherface.