Research On Multi-view Human Face Detecting And Tracking In Complex Background Gray-level Images And Video

Human face detection draws more attention for its academic value and the range of application. Face detection is described as to locate the human face in images or video. The technology of in-plane rotated face detection and tracking in gray and complex background is deeply discussed in this paper. The detail is as bellow:AdaBoost training algorithm is improved. This method turns the single threshold into double threshold, and finishes the week classifier training only by going through training samples once to cut the time consuming. And effectiveness is mathematically proved.AdaBoost samples weights updating algorithm is improved. Add a parameter which describes the performance of the classifier to reduce the over fit while the weights of samples are being updated. A mathematically prove is given for the up bound of the learning error.The Haar-like features are extended. And the 45°integral image is adopted to extract more powerful features and meet the needs of multi-view face detection.The hierarchical decision tree is proposed to detect multi-view face. In training, various error cost is given to different kinds of samples when updating the samples' weights to meet the different requirement of different classifiers' layer performance.In aspect of face tracking, gray geometric data is added when images are sampled by Mean-shift algorithm in order to make the tracking performance more robust in complex background. The convergence of the self-adaptive kernel scale of Mean-shift algorithm which is added with scale dimension is mathematically proved.