Abstract
Human perception of the face involves the observation of both coarse (global) and detailed (local) features of the face to identify and categorize a person. Face categorization involves finding common visual cues, such as gender, race and age, which could be used as a precursor to a face recognition system to improve recognition rates. In this paper, we investigate the fusion of both global and local features for gender classification. Global features are obtained using the principal component analysis (PCA) and discrete cosine transformation (DCT) approaches. A spatial local binary pattern (LBP) approach augmented with a two-dimensional DCT approach has been used to find the local features. The performance of the proposed approach has been investigated through extensive experiments performed on FERET database. The proposed approach gives a recognition accuracy of 98.16% on FERET database. Comparisons with some of the existing techniques have shown a marked reduction in number of features used per image to produce results more efficiently and without loss of accuracy for gender classification.