Block Based Curvelet Feature Extraction for Face Recognition

In this paper, an efficient local appearance feature extraction method based the multi-resolution Curve let transform is proposed for face recognition. Each face is described by a subset of band filtered images containing block-based Curve let coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. The proposed method is compared with some related feature extraction methods such as Principal component analysis (PCA), as well as Linear Discriminant Analysis LDA and Boosted LDA (BLDA). Two different muti-resolution transforms, Wavelet (DWT) and Contour let, were also compared against the Block Based Curve let algorithm. Experimental results on ORL, Yale and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.