LOCAL ZERNIKE MOMENTS: A NEW REPRESENTATION FOR FACE RECOGNITION

In this paper, we propose a new image representation called Local Zernike Moments (LZM) for face recognition. In recent years, local image representations such as Gabor and Local Binary Patterns (LBP) have attracted great interest due to their success in handling difficulties of face recognition. In this study, we aim to develop an alternative representation to further improve the face recognition performance. We achieve this by utilizing Zernike Moments which have been successfully used as shape descriptors for character recognition. We modify global Zernike moments to obtain a local representation by computing the moments at every pixel of a face image by considering its local neighborhood, thus decomposing the image into a set of images, moment components, to capture the micro structure around each pixel. Our experiments on FERET face database reveal the superior performance of LZM over Gabor and LBP representations.