Expression invariant face recognition using contourlet transform

The performance of many state-of-the-art expression invariant face recognition systems hampers when fewer faces are available in the gallery. This paper addresses the issue of expression invariant face recognition with small gallery set. The contourlet transform is an established tool for capturing contour-like edges. The contourlet transform generates prominent features as its local and directional properties have strong resemblance with human visual cortex. We have proposed a novel approach that fuses the features from spatial domain and contourlet transform domain. Feature extraction is performed by employing the LBP and WLD descriptors. The experiments are performed on the JAFFE face database and the Yale face database. The results indicate that the proposed feature level fusion approach yields a robust feature vector and exemplary recognition rates.