Integrating Face and Gait for Human Recognition

This paper introduces a new video based recognition method to recognize non-cooperating individuals at a distance in video, who expose side views to the camera. Information from two biometric sources, side face and gait, is utilized and integrated for recognition. For side face, we construct Enhanced Side Face Image (ESFI), a higher resolution image compared with the image directly obtained from a single video frame, to fuse information of face from multiple video frames. For gait, we use Gait Energy Image (GEI), a spatio-temporal compact representation of gait in video, to characterize human walking properties. The features of face and the features of gait are obtained separately using Principal Component Analysis (PCA) and Multiple Discriminant Analysis (MDA) combined method from ESFI and GEI, respectively. They are then integrated at match score level. Our approach is tested on a database of video sequences corresponding to 46 people. The different fusion methods are compared and analyzed. The experimental results show that (a) Integrated information from side face and gait is effective for human recognition in video; (b) The idea of constructing ESFI from multiple frames is promising for human recognition in video and better face features are extracted from ESFI compared to those from original face images.