基于弹性图匹配的实时视频流人脸识别

This paper deals with the problem of face recognition from video streams based on Elastic Graph Matching (EGM)method. First, instead of manually selecting the feature points as in previous methods, they are automatically selected through feature selection and feature ordering algorithm and correspondingly weighted. Comparing the auto selected feature points with those manually selected from experiences, traditional empirical understanding for feature point selection is corrected. Second, in order to enhance the robustness of the system, the common behavior of the system under uneven illumination, occlusion or remarkable local distortion situation is discussed, based on which a novel graph similarity function that deals with the three situations uniformly is defined, in which failure points give no contribution to similarity score so that effectively enlarges the between class distance and results in enhanced robustness of face recognition. Finally we replace EGM with AdaBoost and Simple DAM in face location and feature alignment stage together with reduced feature points resulted from feature selection based on the characteristics of video streams to speed up the system significantly. The experiment on a video database of 50 persons shows its feasibility.