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Motion transition is a useful method for reusing existing motion data. It generates a seamless intermediate motion with two short motion sequences. In this paper, the Uniform Posture Map (UPM) is proposed to perform motion transitions. The UPM is organized through the quantization of various postures with an unsupervised learning algorithm; it places the output neurons with similar postures in adjacent positions. Using this property, an intermediate posture of two applied postures is generated; the generating posture is used as a key-frame to make an interpolating motion. The UPM has lower computational costs in comparison with other motion transition algorithms. It provides a control parameter; an animator can not only control the motion simply by adjusting this parameter, but can also produce animation interactively. The UPM prevents the generating of invalid output neurons to present unrealistic postures in the learning phase; thus, it makes more realistic motion curves. Finally, it contributes to the making of more natural motions. The motion transition algorithm proposed in this paper can be applied to various fields such as real-time 3D games, virtual reality applications, and web 3D applications.

Biometric systems are forms of technology that use unique human physical characteristics to automatically identify individuals. They have sensors to pick up physical characteristics, convert them into digital patterns, and compare them with patterns stored for individual identification. However, lip print recognition has been less developed than the recognition of other human physical attributes such as the fingerprint, voice patterns, retinal blood vessel patterns, or the face. Lip print recognition by a CCD camera has the merit of being linked with other recognition systems such as the retinal/iris eye and the face. A new method using multi-resolution architecture is proposed to recognize a lip print from pattern kernels. A set of pattern kernels is a function of some local lip print masks. This function converts the information from a lip print into digital data. Recognition in the multi-resolution system is more reliable than recognition in the single-resolution system. Multi-resolution architecture allows us to reduce the false recognition rate from 15 to 4.7%. This paper shows that a lip print is sufficiently used by the measurements of biometric systems.