A feature extraction algorithm is introduced for face recognition, which efficiently exploits the local spatial variations in a face image utilizing curvelet transform. Although multi-resolution ideas have been profusely employed for addressing face recognition problems, theoretical studies indicate that digital curvelet transform is an even better method due to its directional properties. Instead of considering the entire face image, an entropy-based local band selection criterion is developed for feature extraction, which selects high-informative horizontal bands from the face image. These bands are segmented into several small spatial modules to capture the local spatial variations precisely. The effect of modularization in terms of the entropy content of the face images has been investigated. Dominant curvelet transform coefficients corresponding to each local region residing inside the horizontal bands are selected, based on the proposed threshold criterion, as features, which not only drastically reduces the feature dimension but also provides high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentation is carried out upon standard face databases and a very high degree of recognition accuracy is achieved even with a simple Euclidean distance based classifier. 1. Introduction Automatic face recognition has widespread applications in security, authentication, surveillance, and criminal identification. Conventional ID card and password-based identification methods, although very popular, are no more reliable as before because of the use of several advanced techniques of forgery and password-hacking. As an alternative, biometric, which is defined as an intrinsic physical or behavioral trait of human beings, is being used for identity access management [1]. The main advantage of biometric features is that these are not prone to theft and loss and do not rely on the memory of their users. Moreover, biometrics, such as palm-print, finger-print, face, and iris, does not change significantly over time and it is difficult for a person to alter own physiological biometric or imitate that of other person’s. Among physiological biometrics, face is getting more popularity because of its nonintrusiveness and high degree of security. Moreover, unlike iris or finger-print recognition, face recognition does not require high precision equipments and user agreement, when doing image acquisition, which make face recognition even more
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