While the area of face recognition has been extensively studied in recent years, it remains a largely open problem, despite what movie and television studios would leave you to believe. Frontal, still face recognition research has seen a lot of success in recent years from any different researchers. However,the accuracy of such systems can be greatly diminished in cases such as increasing the variability of the database,occluding the face, and varying the illumination of the face. Further varying the pose of the face (yaw, pitch, and roll) and the face expression (smile, frown, etc.) adds even more complexity to the face recognition task, such as in the case of face recognition from video. In a more realistic video surveillance setting, a face recognition system should be robust to scale, pose, resolution, and occlusion as well as successfully track the face between frames. Also, a more advanced face recognition system should be able to improve the face recognition result by utilizing the information present in multiple video cameras.
We approach the problem of face recognition from video in the following manner. We assume that the training data for the system consists of only still image data, such as passport photos or mugshots in a real-world system. We then transform the problem of face recognition from video to a still face recognition problem. Our research focuses on solutions to detecting, tracking and extracting face information from video frames so that they may be utilized effectively in a still face recognition system.
We have developed four novel methods that assist in face recognition from video and multiple cameras. The first uses a patch-based method to handle the face recognition task when only patches, or parts, of the face are seen in a video, such as when occlusion of the face happens often. The second uses multiple cameras to fuse the recognition results of multiple cameras to improve the recognition accuracy. In the third solution, we utilize multiple overlapping video cameras to improve the face tracking result which thus improves the face recognition accuracy of the system. We additionally implement a methodology to detect and handle occlusion so that unwanted information is not used in the tracking algorithm. Finally, we introduce the average-half-face, which is shown to improve the results of still face recognition by utilizing the symmetry of the face. In one attempt to understand the use of the average-half-face in face recognition, an analysis of the effect of face symmetry on face recognition results is shown.
