Face Recognition System Design And Implementation

A framework of application system development for computer vision is proposed in this paper, after researching the currenty classical algorithms for face detection and recognition. And then, a system prototype is implemented centered by the tasks of "Face Detection" and "Face Recognition".Firstly, the face detection method presented by Viola et al based on Haar-like feature and AdaBoost algorithm is improved. The original discrete AdaBoost algorithm is replaced with real AdaBoost one which allows the weak classifiers have confidences in real values as outputs. It accelerates the algorithm' s convergence. Accordingly, the original weak classifier with a single threshold value is substituted by Feature Search Table (FST) whose outputs can be suit for almost any distribution.Secondly, several subspace methods for dimension reduction and its applications in face recognition are discussed, including linear (PCA and LDA) and non-linear (manifold learning) algorithms, and then, the experimental results are shown.Thirdly, the designs of two classifiers are described, including BP neural network and support vector machine. And a novel method for image matching based on Partial Hausdorff Distance (PHD) is proposed and combined with BP neural network and support vector machine, that is, match the frontal candidates which have the largest probabilities with the face image to be recognized, and select the best one for output.Finally, some implementation details of the face recognition system and the GUI design are presented.