Study On Fast Multi-Pose Face Detection In Complex Background

Face detection can be widely used in many fields such as human-machine interaction, content-based retrieval, digital video compression and video surveillance. Face detection, which is also a hot topic in domestic and foreign research areas in recent years, is the most basic and very important technique for all sorts of face processing systems. However, there are still difficulties for multi-pose face detection under complex background, and only a few effective methods can be found at present.This dissertation focuses on multi-pose face detection under complex background and some correlative fast algorithms. Following the main clue of improving face detection speed, this dissertation has studied each tache of multi-pose face detection under complex background, and proposed some fast algorithms. Then the schemes for fast multi-pose face detection in gray and color image are designed respectively. Actually, this dissertation makes some detail contributions as follow:Firstly, some correlative fast algorithms are presented, aiming at each tache of face detection. As it is very time consuming for grayscale distribution normalization before template matching, a fast algorithm is presented for grayscale distribution normalization based on extended integral image. This algorithm reduces greatly the time for calculating the grayscale average and variance of image windows. In order to make full use of the image grads and grads direction feature for face detection, multi-direction edge detection operators of Kirsch is used. Therefore, a fast algorithm of Kirsch edge detection based on templates decomposition and integral image is proposed for solving the time consuming problem of multi-direction edge detection. Since face detection and organ locating with integral projection and variance projection are in common use, a fast projection algorithm based on extended row-column integral image is presented, and then the calculating time is reduced effectively for the row (or column) integral projection and variance projection of image windows. In addition, aimed at improving the efficiency of multi-template matching, a fast multi-template matching algorithm based on extended integral image is presented. For application of template matching with mask, a fast template matching algorithm based on extended integral image with mask is proposed, and the detailed application in multi-pose face template matching is illustrated.Secondly, aimed at solving the problem of detecting multi-pose face in gray image under complex background, a fast face detection algorithm is proposed based on multi-pose knowledge models and templates. At first, a face organ grads image is extracted from the original image, and multi-pose knowledge models and the multi-pose face templates are set up. Then coarse face detection is implemented by multi-pose knowledge models and correlative rules, and fine face detection is done by multi-pose face template matching. Finally the position and size of each face are captured in image, and the coarse pose of each face is estimated by the triangle with center of gravity of eyes and mouth. The organ grads feature pixels in random rectangle areas is summed up rapidly by integral image in multi-pose knowledge models. The local and whole matching between multi-pose templates and image windows is implemented by fast multi-pose templates matching algorithm proposed in this dissertation.Thirdly, aimed at solving the problem of detecting multi-pose face in color image under complex background, a fast multi-pose face detection algorithm based on multi-threshold feature fusion is presented. Multi-threshold organ grads image and grads direction image are extracted by face organ grads feature and Kirsch edge detection operators from the original image, double-threshold skin image is extracted by face skin feature, and gray feature image is extracted by lightness information. Then the feature fusion models are set up, and multi-pose knowledge models and multi-pose face templates are used to accomplish face detection. In face detection processing, coarse-to-fine strategy and correlative fast algorithms in each tache are used to improve the detection speed, several features for distinguishing face under complex background are fused to improve the detection accuracy, and the missed faces are reduced by fusing information of the same feature in different thresholds. In addition, based on the fact that human eyes can detect face easily from gray image without skin color information, windows with obvious grads feature of face pattern are allowed to enter the next face classifier without skin color detection, so the missed faces with skin color distortion can be reduced.