Gender classification using GA-based adjusted order PZM and fuzzy similarity measure

An important problem in gender classification system is dealing with facial expression variations, lighting direction changes, noise presence and etc. In this paper, a new patch based method is proposed for gender classification under above conditions and when one sample from each person is available. A genetic algorithm based adjusted order Pseudo-Zernike Moment (PZM) is used to extract features of each face area. In the proposed method, a weighting scheme is utilized to determine the importance of each local area. Finally, the similarity between input image and gallery images is calculated by fuzzy similarity measure. The satisfactory experimental results show the high recognition rate of our method on the AR and FERET face databases compared to recent available approaches.