Gender recognition based on local body motions

Human body motions, including gait information, are a promising biometrics resource. In this paper, the human silhouette is segmented into seven components for visual surveillance applications, namely, head, arm, body, thigh, front-leg, back-leg, and feet. The legs are classified as front-leg or back-leg because of the bipedal walking style: during walking, the left-leg and the right-leg are in front or at the back in turn. The motions of the individual components and of a number of combinations of components are then studied for gender recognition. For HumanID recognition under different cases, the performances of and underlying links amongst the seven human gait components are analyzed.

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