A comparative analysis of feature extraction methods for face recognition system

Recognizing face images due to changes in illumination condition, pose, facial expression and others are challenging task. Solving these problems requires a feature extraction method that can generate distinct features for each class of image. Hence, this paper describes the comparative analysis of feature extraction methods namely geometric moments, Zernike moments, Krawtchouk moments and principle component analysis (PCA) in terms of their capability to recognize face images. The classification technique employed in the recognition stage is back propagation neural network (BPNN). The experiments utilized database face images from Olivetti research laboratory (ORL) consisting of 40 subjects of 10 samples each where none of them are identical. They vary in position, rotation, scale and expression. From the comparative study, the most suitable feature extraction method is considered for face recognition system.