Research On Automatic Face Recognition And It's Implementation In Identity Authentication System

Automatic Face Recognition (AFR) is challenging in image processing and analyzing. By AFR, we mean that people attempt to endow computers with the ability to analyze the human face image, to extract the valid individual information, and to identify him/her. Such a type of theory and technology is not only greatly desired in the theoretical research but also has significant potential in applications.After the nearly tens years' research, AFR technology has already obtained considerable advances especially in the past several years. The existing AFR commercial systems can basically meet the application need under certain strict constraints. Thus, it is far from the true trend practical level in the non-ideal controllable situation.As one of the main research targets of the Application Material Foundation of Shanghai-"Research on Self-Organization Safety Surveillance System Based on ARM and RFID" under the project grant number 0512, starting from the key issues which need to be solved in AFR systems, this study plays emphasis on the real-time face detecting and tracking, the face key features localization, the highly effective person face representation, the robust human face recognition classifiers and AFR system design and so on.Face Detection is a key issue which needs to be solved in AFR system design. Many algorithms have been proposed in the few past decades. The algorithm based on the Haar-like Rectangle Feature Cascade Strong Classifiers proposed by Viola in 2001 symbolized the human face detection start to move towards practical. This algorithm detects human faces by extracting the Haar-like features of a human face and training a classifier. It only uses the gradation value without considering the skin color distribution of a human face. Thus it has poor robustness to those face structure-like objects under complex background. In view of this, we propose a real-time face detection algorithm based on skin color model verification and the Haar-like features cascade strong classifier (SCC-HCC) in the third chapter.Face Tracking is an essential stage in those applications, such as video-based human face recognition, video-based monitoring. The CamShift algorithm has strong robustness regarding the object tracking, but it has the drawbacks which Tracking-window must be initialized manually, and the robustness to those skin color-like regions is unsatisfactory. In the third chapter of this thesis, we propose a real-time face tracking algorithm through making improvements to CamShift, named AM-CamShift, which can initialize the track window and implement the multi-objects tracking automatically, and enhance the robustness to skin color-like area.The linear discriminant analysis (LDA), especially the Fisher linear discriminant analysis...