Fuzzy Aggregation Method Using Fisherface and Wavelet Decomposition for Face Recognition
Keun-Chang Kwak*,**, Witold Pedrycz**,***, Hyoun-Joo Go*, and Myung-Geun Chun*
*School of Electrical and Electronic Engineering, Chungbuk National University, 48 Gaeshin-dong Heungduk-gu Chongju 361-703, Korea
**Dept. of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2G7
***Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland
In this paper, we propose the fuzzy aggregation method for face recognition based on subimage sets decomposed by wavelets. The proposed approach consists of four main stages. The first stage uses the wavelet decomposition that helps extract intrinsic features of face images. The second stage of the approach applies a fisherface method to these four subimages obtained by wavelet decomposition. The choice of the fisherface method in this setting is motivated by its insensitivity to large variation in light direction, face pose, and facial expression. The last two phases are concerned with the aggregation of the individual classifiers by means of the fuzzy integral. The experiments use an n-fold cross-validation to assure high consistency of the classification results. The experimental results obtained for the Yale face databases reveal that the approach presented in this paper yields better classification performance in comparison with the results obtained by other recognition methods.