Face recognition using curvelet based PCA

This paper identifies a novel feature space to address the problem of human face recognition from still images. This is based on the PCA space of the features extracted by a new multiresolution analysis tool called Fast Discrete Curvelet Transform. Curvelet Transform has better directional and edge representation abilities than widely used wavelet transform. Inspired by these attractive attributes of curvelets, we introduce the idea of decomposing images into its curvelet subbands and applying PCA (Principal Component Analysis) on the selected subbands in order to create a representative feature set. Experiments have been designed for both single and multiple training images per subject. A comparative study with wavelet-based and traditional PCA techniques is also presented. High accuracy rate achieved by the proposed method for two well-known databases indicates the potential of this curvelet based feature extraction method.