• Journal of Multimedia Information System
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• v.3 no.2
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• pp.9-12
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• 2016

This paper is concerned with the effective and efficient identification of the gender and motion of humans. Tracking this nonverbal behavior is useful for providing clues about the interaction of different types of people and their exact motion. This system can also be useful for security in different places or for monitoring patients in hospital and many more applications. Here we describe a novel method of determining identity using machine learning with Microsoft Kinect. This method minimizes the fitting or overlapping error between an ellipsoid based skeleton.

• Journal of the Korea Computer Graphics Society
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• v.8 no.4
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• pp.1-7
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• 2002

This paper presents a new approach for modeling human limbs shape from 3D scan data. Based on the cylindrical structure of limbs, the overall shape is approximated with a set of ellipsoids through ellipsoid fitting and interpolation of fit-ellipsoids. Then, the smooth domain surface representing the coarse shape is generated as the envelope surface of ellipsoidal sweep, and the fine details are reconstructed by constructing parametric displacement function on the domain surface. For fast calculation, the envelope surface is approximated with ellipse sweep surface, and points on the reconstructed surface are mapped onto the corresponding ellipsoid. We demonstrate the effectiveness of our approach for skeleton-driven body deformation.