A feature extraction algorithm is introduced for face recognition, which efficiently exploits the local spatial variations in a face\r\nimage utilizing curvelet transform. Although multi-resolution ideas have been profusely employed for addressing face recognition\r\nproblems, theoretical studies indicate that digital curvelet transform is an even better method due to its directional properties.\r\nInstead of considering the entire face image, an entropy-based local band selection criterion is developed for feature extraction,\r\nwhich selects high-informative horizontal bands from the face image. These bands are segmented into several small spatial modules\r\nto capture the local spatial variations precisely. The effect of modularization in terms of the entropy content of the face images has\r\nbeen investigated. Dominant curvelet transform coefficients corresponding to each local region residing inside the horizontal\r\nbands are selected, based on the proposed threshold criterion, as features, which not only drastically reduces the feature dimension\r\nbut also provides high within-class compactness and high between-class separability. A principal component analysis is performed\r\nto further reduce the dimensionality of the feature space. Extensive experimentation is carried out upon standard face databases\r\nand a very high degree of recognition accuracy is achieved even with a simple Euclidean distance based classifie
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