On Combining Dissimilarity-Based Classifiers to Solve the Small Sample Size Problem for Appearance-Based Face Recognition

For high-dimensional classification tasks, such as face recognition, the number of samples is smaller than the dimensionality of the samples. In such cases, a problem encountered in Linear Discriminant Analysis-based (LDA) methods for dimension reduction is what is known as the Small Sample Size (SSS) problem. A number of LDA-extension approaches that attempt to solve the SSS problem have been proposed in the literature. Recently, a different way of employing a dissimilarity representation method was proposed [18], where an object was represented based on the dissimilarity measures among representatives extracted from training samples instead of the feature vector itself. Apart from utilizing the dissimilarity representation, in this paper, a new way of employing a fusion technique in representing features as well as in designing classifiers is proposed in order to increase the classification accuracy. The proposed scheme is completely different from the conventional ones in terms of the computation of the transformation matrix as well as the selection of the number of dimensions. The present experimental results demonstrate that the proposed combining mechanism works well and achieves further improved efficiency compared with the LDA-extension approaches for well-known face databases involving AT&T and Yale databases. The results especially demonstrate that the highest accuracy rates are achieved when the combined representation is classified with the trained combiners.