Recognition And Feature Extraction

Face Recognition technology is an important brance of Biometrics and it is also the study focus of Pattern Recognition and Computer Vision. Face detection and location and face feature extraction are the two parts of the Face Recognition system, the following is our main work in this paper :1. Face Recognition study has two directions: the method based on the whole face and the method uses feature analysis. the method uses the K-L convolution and the method based on apparatus' features will be presented to extract the feature, then we use the least distance classifier and the nearest neighbour distance classifier to match the face images. We give an improved histogram equalization algorithm for image enhancement together.2. The Eigen Face method, which is based on the theory of Principle Component Analysis(PCA), treats the face image as a vector and get Eigenvalue vectors using K-L convolution. The linear combinations of these Eigen Vectors are used to describe, represent, and approach the face image. Thus feature can be extracted.3. We also use the character of apparatus to set up models, such as eyes-modeling, mouth-modeling, chin-modeling and so on. With the aid of the face normalization model, feature could be extracted. We adopt the nearest neighbour distance classifier to match the simulation.In the end, we design one recognition system. We get the ideal result and our methods are proved to be perfect.