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This paper proposes an automatic gait recognition approach for analyzing and classifying human gait by computer vision techniques. The approach attempts to incorporate knowledge of the static and dynamics of human gait into the feature extraction process. The width vectors of the binarized silhouette of a walking person contain the physical structure of the person, the motion of the limbs and other details of the body are chosen as the basic gait feature. Different from the model-based approaches, the limb angle information is extracted by analyzing the variation of silhouette width without needing the human body model. Discrete Cosine analysis is used to analyze the shape and dynamic characteristic and reduce the gait features. And this paper uses the multi-class support vector machines to distinguish the different gaits of human. The performance of the proposed method is tested using different gait databases. Recognition results show this approach is efficient.

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