Technology Research Of Multi-Pose Face Detection And Tracking

Multi-pose face detection and tracking is becoming a significant field of research in computer vision and pattern recognition. It has great application prospects in video conference, human-computer interaction, image retrieval, intelligent video surveillance, and so on. However the issue of its accuracy and real-time detection is urgent needed to solve.In this paper, we introduce a method of multi-view face detection and tracking for human faces in the video stream, combining facial image preprocessing and post-tracking algorithm and face detection algorithm based on Adaboost is improved. Firstly, the situation and basic method of processing of video image is introduced; Secondly, the face detection frame design in the video stream and implementation of the code is showed; finally, the face detection system of performance is verified, and we analyze the result.Research and realization of the Multi-view face detection and tracking system play the foremost important role in this paper, we research face detection algorithm based on Adaboost, Camshift algorithm, Kalman filter and so on, which is included three sub-modules:(1) Model of image preprocessing and pre-estimation of moving object:image preprocessing module gives some introduction about image filtering algorithm, morphology algorithm and image normalization algorithm; moving object estimation module mainly introduces the Gaussian mixture model algorithm based on background substraction.We use this method to obtain the foreground movingobjects, and then to extract its contours and set them as our regions of interest so as to narrow search window to detect faces.(2) Model of face detection:We study the principle of the Adaboost algorithm particularly and expound theories of haar features, integral image and cascade, it makes a good performance in detection speed and accuracy, but meanwhile it also has a high error rate. We analyse the weekness of the algorithm, this paper presents an algorithm which is named PSO-Adaboost to improve face detection accuracy with algorithm thinking of support vector machines and particle swarm optimization algorithm for Adaboost-based algorithm. in addition, at first we predict face region by skin color model and then detect face in the face region.(3) Model of face tracking:We propose a new algorithm on the basis of Camshift and Canny edge detection, and morphology algorithm, kalman filter is also used to make face tracking accuracy the better in our face tracking system.