This paper identifies a novel feature space to

address the problem of human face recognition from

still images. This 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.-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.