An analysis of the gender and age differentiation using facial parts

The main purpose of our research is to evaluate which subject's facial parts are most effective at making the difference between men and women. We prepared the four directional feature fields on multiple facial parts images, such as face, jaw, lip, nose, eyes, and R eye. Then, we recognized these images by using the linear discriminate analysis. Furthermore, we analyzed the feature space by using within-class variance between-class variance ratio. After experimenting on a large number of subjects, we conclude that face and jaw make good feature spaces. Moreover, we discovered that recognition rate changes with ages. Accordingly, a possibility that each man and woman had the age, which is easy to distinguish gender, was suggested.