Research On Face Detection And Tracking In Video-based Images

Face detection and tracking is an important research aspect in artificial intelligence and computer vision. As a key technology of face information processing, it has a broad application values in many fields such as video surveillance, content-based image retrieval, videoconference, etc.In this thesis, a study on face detection and tracking in video is presented. After a general review of existing schemes on this particular topic, we choose Wavelet feature as the main feature of human face and Adaboost training algorithm to construct the face detectore, due to the real-time requirement of video. We study two face detection approaches including histogram-based statistical learning approach and Haar-like feature-based face detection approach. By using 5/3 wavelet transformation, the former one could effectively decompose the image in frequency, orientation, space, and geometry, obtaining overcomplete feaure set and detecting frontal and profile faces effectively; While The latter utilize integral image to quickly calculate the feature, and construct weak classifier by the feature; then weak classifiers are combined to a strong classifier in a linear way.The final classifier is built in a cascade structure which could reject most non-face samples in the early layer.Two face tracking methods with different emphasis are also studied in this thesis. The first one accomplish face tracking in viedo by utilizing histogram-based statistical learning approach to detect faces in face-candidate regions, which was obtained by using skin pre-process and motion information in video. The second one applies Mean-shift object tracking algorithm and Kalman filtering to face tracking. The central computational module is based on the mean shift iterations and target model updating and finds the most probable face target position in current frame.The contributions of this paper could be expressed as the following 3 aspects:(1) Efficient skin segmentation pre-processing algorithm; improve the current lighting compensation algorithm;propose a new region segmentation and combination algorithm especially for skin binary image;(2) Present a method to quantize wavelet parameters and concept of quantization in different groups; change the output of weak classifier in Adaboost training and provide a method to set the threshold of the best weak classifier; propose a sample statistic-based criterion to set the threshold of face classifier;(3) A novel real-time face tracking algorithm was presented based on Mean-Shift target tracking algorithm and Kalman filtering algorithm; propose a new thinking to detect and track face in real-time by combining Adaboost face detection algorithm and Mean-Shift algorithm with pose estimation implemented to the tracked face.Experiment results and comparison with other published method show that algorithm in this thesis obtains almost ideal result in the field of detection accuracy rate, false alarm rate and detection and tracking speed, and both of these two algorithms are complete, robust and efficient face detection and tracking algorithms with great comprehensive performance.