Biometrics makes use of the physiological or behavioral characteristics of people to authenticate their identities. With a growing need for a full range of visual surveillance and monitoring systems in security-sensitive environments such as banks and airports, human identification at a distance has recently gained increasing interest from computer vision researchers.To operate successfully, the established biometrics such as face, fingerprint and iris usually require proximal sensing or physical contact. However, they are hardly applicable at a distance. Fortunately, gait, the way people walk, is still visible and can be easily perceived unobtrusively. So, from a surveillance perspective, gait is a very attractive modality. Gait recognition is a relatively new research direction. It aims to seek distinguishable variations between the same actions of walking from different people for the purpose of automatic identity verification. Focusing on this topic, this dissertation mainly includes the following issues: ① Based on the idea that a specific appearance model can be learned from spatial-temporal motion pattern of gait, we propose a simple and efficient gait recognition algorithm using statistical shape analysis. For each image sequence, an improved background subtraction procedure is used to extract moving silhouettes of the walker from the background. Temporal changes of thc detected silhouettes are then represented as an associated sequence of complex vector configurations in a common coordinate frame, and are further analyzed using the Procrustes shape analysis method to obtain an eigen-shape as signatures. This method does not directly analyze the dynamics of gait, but implicitly uses the action of walking to capture the structural characteristics of gait, especially the biometric shape cues. Experimental results demonstrate that the proposed algorithm has an encouraging recognition performance. ② Based upon an intuitive consideration that recognizing people by gait depends greatly on how the silhouette of human body changes over time, we present a non-parametric gait recognition method using PCA (Principal Component Analysis) . For each image sequence, a background subtraction algorithm and a simple correspondence procedure are first used to segment and track the moving silhouettes of a walking figure. Then, eigenspace transformation based on the PCA is applied to 1D time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification tech- niques are finally performed in the lower-dimensional eigenspace for recognition. This method implicitly captures the structural and transitional characteristics of gait. Extensive experimental
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