Fusion of static and dynamic body biometrics for gait recognition

• Published in: IEEE transactions on circuits and systems for video technology Vol. 14; no. 2; pp. 149 - 158
• Main Authors: Wang, L., Ning, H., Tan, T., Hu, W.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.2004; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Biometrics; Computer science; control theory; systems; Computer vision; Dynamical systems; Dynamics; Exact sciences and technology; Face recognition; Human; Humans; Image processing; Information analysis; Information, signal and communications theory; Legged locomotion; Mathematical models; Pattern recognition. Digital image processing. Computational geometry; Recognition; Shape; Signal processing; Studies; Surveillance; Telecommunications and information theory; Terrorism; Trajectory; Walking; Biometrics; Computer vision; Target tracking; Image recognition; gait recognition; Shape analysis; Gait; Identification; Pattern recognition; tracking; Index Terms-Biometrics; joint-angle trajectory; Procrustes shape analysis; Classification; Feature extraction
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2003.821972

Vision-based human identification at a distance has recently gained growing interest from computer vision researchers. This paper describes a human recognition algorithm by combining static and dynamic body biometrics. For each sequence involving a walker, temporal pose changes of the segmented moving silhouettes are represented as an associated sequence of complex vector configurations and are then analyzed using the Procrustes shape analysis method to obtain a compact appearance representation, called static information of body. In addition, a model-based approach is presented under a Condensation framework to track the walker and to further recover joint-angle trajectories of lower limbs, called dynamic information of gait. Both static and dynamic cues obtained from walking video may be independently used for recognition using the nearest exemplar classifier. They are fused on the decision level using different combinations of rules to improve the performance of both identification and verification. Experimental results of a dataset including 20 subjects demonstrate the feasibility of the proposed algorithm.