A weighted pseudo-zernike feature for face recognition
Shahpour Alirezaee, Majid A. Ahmadi, Hassan Aghaeinia, R. Rashidzadeh · 2006
Pseudo-Zernike polynomials are well known and widely used in the analysis of optical systems. In this paper, we introduce a weighted pseudo-Zernike feature for face recognition. The EA strategy is used to maximize the Fisher linear discriminant function (FLD) over the Pseudo-Zernike moments. The argument, which maximizes the FLD criteria, is selected as the proposed weight function. To evaluate the performance of the proposed feature, experimental studies are carried out on the ORL database images of Cambridge University. The numerical results show 97.75% recognition rate on the ORL database with the weighted pseudo-Zernike feature (with order 10) and 65, 146,40 neurons for the input, hidden, and output layers while this amount for the original pseudo-Zernike is 96.5%