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Discriminant analysis and similarity measure
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This paper presents a method for effective use of color information and a new similarity measure with application to large scale face verification. Specifically, three effective color component images are first obtained from a new color model that takes advantage of the subtraction of the primary colors. A compact color image representation is then derived through discrete cosine transform and feature selection for redundancy reduction and computational efficiency. The effective color features in terms of class separability are further extracted by means of discriminant analysis. A new similarity measure is finally presented for improving pattern recognition performance. The effectiveness of the proposed method is evaluated using a large scale, grand challenge pattern recognition problem, namely, the Face Recognition Grand Challenge (FRGC) problem. Specifically, the experiments using 36,818 FRGC color images show that the new color model improves upon other image modalities, such as the RGB color image, and the grayscale image; and the new similarity measure consistently performs better than other popular similarity measures, such as the Euclidean distance measure, the cosine similarity measure, and the normalized correlation.