Study On Face Detection, Tracking And Recognition In Color Image Sequences

Human face is our primary focus of attention in social intercourse, playing a major role in conveying identity and emotion. Research on the face recognition technology has great theoretical and practical values. The study on face detection, tracking and recognition is becoming an active research subject with the development of computer technology in recent years. Compared with still image, color image sequence provides more information, such as color information, motion information, and so on. However, it should be more robust to different illumination conditions, complex background, face occlusion, and so on. Additionally, it demands lower computational cost. The study on face detection, tracking and recognition in color image sequence is still a challenging task.This thesis focuses only on the key technologies of face detection, tracking and recognition in color image sequence. The main contributions of this thesis are as follows:1. An improved color image enhancement algorithm is presented. Based on Retinex theory, this improved algorithm can automatically determine values for the parameter, instead of looking for fixed value, and decrease the amount of computation. The results on color images show the validity of this improved algorithm. After applying this algorithm to preprocessing in face detection, the detection rate increases slightly.2. A coarse-to-fine approach to face skin detection in color image sequence is proposed. At first, motion regions are extracted from color image sequence in order to discard background region. Once the regions of interest are located, skin color detection is used to get skin color regions. Then edge information and mathematical morphology method are integrated to progressively restrict the regions to smaller areas, as candidate face skin regions.3. A novel self-skin algorithm is proposed for skin detection. The idea of this algorithm is using the watershed method to cluster the pixels in the YCbCr color space, instead of applying conventional skin color model. The experimental results show that this algorithm is robust to different illumination conditions and complex background, and its reliability excels...