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In this paper, we have proposed an approach for face recognition by composing Symlet decomposition, Fisherface algorithm, 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 of this approach uses Fisherface method which is composed of PCA and LDA. The reason for using this was that it is not sensitive to intensive light variations and facial expression and gesture. The third and forth section of this approach, 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 approach presented in this paper, will lead to better classification performance compared to other classification methods.