Neural network face recognition using statistical feature and skin texture parameters

The virtual texture is due to regular or random variation in the gray level or color in an image. Features based on texture are often useful in automatically distinguishing between objects and in finding boundaries between regions. New features that are based on texture analysis of the face skin are proposed as efficient tools for face recognition. In the preprocessing step, the analyzed face region is detected. Then, the texture features of this region, namely, energy, entropy and homogeneity, are extracted. In order to test the performance of the skin texture based features in face recognition we combine them with our previously introduced statistical features that are extracted from a coded image which is obtained from the edge detection of a binary version of the original gray scale image. The statistical features and skin texture parameters are fed to a FBP neural network for face recognition. Computer simulation results with 100 test images of 10 persons (the images of each person in various poses, facial expression, and facial details) show that the proposed skin texture features highly enhance the recognition rate.