A weighted Pseudo-Zernike feature extractor for face recognition

Pseudo-Zernike polynomials are well known and widely used in the analysis of optical systems. In this paper, a weighted Pseudo-Zernike feature is introduced for face recognition. We define a weight function based on the face local entropy. By this weight function, the role of high information region, i.e. eyes, noses and lips, will be intensified on the extracted features. For classification, a single hidden layer feedforward neural network has been trained. To evaluate the performance of the proposed technique, experimental studies are carried out on the ORL database images of Cambridge University. The numerical results show 98.5% recognition rate on the ORL database with the order 8 of weighted Pseudo-Zernike feature and 44, 98, 40 neurons for the input, hidden, and output layers while this amount is 96% for the original Pseudo-Zernike.