Abstract
In the thesis, we employ a Bayesian estimation framework that exploit image patch similarity and accumulated distribution to reach the goal of predicting the gender from the facial images. Our facial gender classification consists of two processes. The first process searches the face candidates using Haar-like feature from the images, and uses the size filter and skin color detection to remove the non-face candidates. In addition, we apply the image filtering and weighting mask to obtain the normalized facial images. The second process uses the predefined library and training images to identify human gender of the test images. The facial images require a large range of variation including lighting, expression, pose, background…etc. We consider that the formation of library images has a close relationship with the accumulated distribution about gender (male, female). We propose a library images selection scheme to choose the discriminative male and female images, which is based on the method of eigenface with K means clustering, then build the male and female accumulated distribution according to the characteristic of the rest part of training male and female images. We evaluate the two library selection method, eigenface with clustering method and random method, and observe the difference of gender prediction accuracy using 1000 web facial images of male and female, the color FERET face database which contain 1364 regular frontal facial images of male and female, and the Bao face database group images of male and female. The experimental results demonstrate that our proposed method is better than the random selection in most cases.