A comparative study of wavelet and Curvelet transform for face recognition

Wavelet transform had been a popular feature extraction tool for face recognition, and much work has been done regarding the choice of wavelet subbands in presence of variation in expression, illumination and pose. However the using of curvelet transform, which is effective in representing curves, and the performance analysis have not been studied. We propose to employ curvelet for facial feature extraction, and perform a thorough comparison against wavelet transform; especially, the orientation of curvelet is analyzed. Experiments show that for expression changes, the small scale coefficients of curvelet outperform the high frequency coefficients of wavelet to a large degree. Whilst for illumination changes, the small scale coefficients of curvelet transform are robust, though the large scale coefficients of both transform are likely influenced. The reason behind the advantages of curvelet lies in its abilities of spatial localization and orientation sensitivity, thus the experiments and theoretical analysis coincide.