Research Of Face Detection And Tracking Based On Real AdaBoost And WMIL Algorithm

Face detection and face tracking are important research themes in the topic of Pattern Recognition and Computer Vision. They have a broad application prospect in many fields such as video surveillance,human face recognition, facial expression recognition,medical diagnosis,human-computer interaction and so on. Take the Video Surveillance Field for example, face detection and tracking has greatly enhanced the Intelligent Video Surveillance System, enables the system to achieve target detection and tracking in the dynamic scene without people. Based on the domestic and foreign academic papers and research reports on human face detection and tracking, the dissertation explores the issue of face detection and tracking with a relatively static background from video sequences. The main results of this paper are summarized as follows:(1)The method takes advantages of both of shin and Real Ada Boost,is able to detect faces with low false negative rate,fast speed and high detection rate. It can achieve more accurate face detection.Before skin color segmentation, the image must be transformed to YCb Cr color space because skin color segmentation is easily influenced by lighting conditions.Then,Because skin color has better clustering in improved color space,skin color model is built to segment the skin color reigns which include candidate human face regions from the image. This process lays a foundation for the following step. Real Ada Boost algorithm is improved by Ada Boost. It can obtain strong learners by combining a series of weak learners. Actual situation of the human face image size is different, so the mechanism of multi-scale detection is introduced.Using cascade Real Ada Boost training and testing, improves the precision of detection.(2)WMIL may fail when the illumination changes drastically or the object of interest occurred full occlusions, this paper presents a new real time face detection and tracking algorithm based on improved WMIL and Ada Boost.The algorithm firstly uses Ada Boost cascade classifier to scan images, the initial tracking window was obtained, and then based on information of face location, in the framework of WMIL,the multi scale image representation was adopted, features were extracted by compression perception. The model and classifier,established by WMIL tracking algorithm,are used for the tracking of face. Experiment results have shown that thealgorithm satisfies the requirement of real-time and improves the deficiencies of WMIL. It effectively solves the problem affected by the appearance changes, the target posture changes, fast moving and other factors. The algorithm acquires continuous and stable performance, and also very good robust tracking.(3)The adoption of single features(Haar-like)describes the target in WMIL algorithm, this character cannot fully represent the target.To a certain extent, it affects the effect of target tracking.Distribution Fields descriptor can not only well represents the image information, but also creates a larger smooth area in the target area.Based on Distribution Fields,this paper represent a WMIL algorithm of online feature fusion.In the framework of WMIL, multi-scale image feature and distribution fields layer are adopted.Based on these two features,this paper trains the corresponding strong classifier.Through adaptive linear fusion algorithm,the paper gets a strong classifier,and realizes robust tracking of single face.Finally, numerous experimental demonstrate that the proposed tracking algorithms can handle the partial or heavily occlusion,motion blur,posture changes and illumination change and other factors, and work well.