Robust hand tracking using a skin tone and depth joint probability model
Corey Mason Manders, Farzam Farbiz, Jonathan Hao Jie Chong, Ka Yin Tang, Gim Guan Chua, Mei Hwan Loke, Miaolong Yuan · 2008
One of the long-term goals in human-computer interaction is to utilize more intuitive and natural methods such as speech and hand gestures that a user would employ for communication. In this paper, we present a robust method of hand tracking using a probability map computed from a joint probability function derived from both depth information and skin-tone information. The depth information is provided using a commercially available stereo camera, and the color information is found using calibrated and linearized color information. The work shows the effectiveness of this technique, in terms of both the quality of the results as well as the speed at which the computations may be performed. Due to the linearization of the color information and the use of stereo vision data, the technique is demonstrated to be largely invariant to illumination changes.