Face Recognition Based On Wavelet And Fisher Face

The technology of face recognition is an active subject in the area of pattern recognition. As a biometrics technology, FRT has numerous applications such as access control, law enforcement, e-commerce, video surveillance and so on.Face image preprocessing and face feature extraction is an important component of the face recognition system. In this paper, we focus on the application of wavelet transform on face image preprocessing and the application of Fisher face method on face feature extraction. Based on the classic method, we make some innovation and improvement and use the method in a real-time face recognition system. The principal works are listed as follows:(1) Wavelet transform is used more and more widely in face recognition, but only approximate image was used for feature extraction in most reference. In fact, horizontal details and vertical details also play a certain role in description of human face, especially in eyes, nose, etc. We propose a novel face image preprocessing method based on weight-wavelet which adds approximate image, horizontal details and vertical details with certain weight coefficient. The experiment result shows that this method makes better recognition rate.(2) Expound the basic principal of Fisher face method, and introduce a new method named RFLD which can which can solve the limited of vector number. But it needs large amount of computation. In this paper, we improve the RFLD method. The experiment result shows that the improved method not only reduces the amount of computation, but also ensures the recognition rate, or even increases the recognition rate.(3) Use proposed method in a real-time face recognition system, prove the feasibility of our method and get satisfactory results.(4) In this paper, the image preprocessing and feature extraction methods are described in detail. We improve the traditional methods and analyze the advantage and disadvantage of our methods, and propose the direction and objective in the future.