Abstract
This chapter introduces a 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, an enhanced side face image (ESFI), a higher resolution image compared with the image directly obtained from a single video frame, is constructed, which integrates face information from multiple video frames. For gait, the gait energy image (GEI), a spatio-temporal compact representation of gait in video, is used to characterize human walking properties. The features of face and gait are obtained separately using the principal component analysis (PCA) and the multiple discriminant analysis (MDA) combined method from ESFI and GEI, respectively. They are then integrated at the match score level by using different fusion strategies. The approach is tested on a database of video sequences, corresponding to 45 people, which are collected over seven months. The different fusion methods are compared and analyzed. The experimental results show that (a) better face features are extracted from ESFI compared to those from the original side face images; (b) the synchronization of face and gait is not necessary for face template ESFI and gait template GEI. The synthetic match scores combine information from them; and (c) integrated information from side face and gait is effective for human recognition in video.
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Bhanu, B., Han, J. (2010). Match Score Level Fusion of Face and Gait at a Distance. In: Human Recognition at a Distance in Video. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-124-0_10
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DOI: https://doi.org/10.1007/978-0-85729-124-0_10
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