Using subclass discriminant analysis, fuzzy integral and symlet decomposition for face recognition

In this paper, an approach has been proposed for face recognition by composing symlet decomposition, Subclass Discriminant Analysis (SDA), and Sugeno and Choquet Fuzzy Integral. This approach consists of four main sections: the first section uses Symlet, one of the Wavelet families, to transform an image into four sub-images which are called approximate, horizontal, vertical and diagonal partial images respectively. The aim of this work is to extract intrinsic facial features. The second section is composed of PCA and SDA. The reason for using this method was the fact that it is not sensitive to intensive light variations. The third and forth section of this paper are related to the aggregation of the individual classifiers by means of the fuzzy integral. Both Sugeno and Choquet fuzzy integral are considered as methods for classifier aggregation. In this paper, Olivetti Research Labs face database is used for acquiring experimental results. The combination of this method with sym decomposition has yielded a recognition rate which is equal to 97.5 %. The approach presented in this paper, will lead to better classification performance compared to other classification methods.