Segmenting hands of arbitrary color

Xiaojin Zhu, Jie Yang, A. Waibel
Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)  
Color has been widely used for hand segmentation. However, many approaches rely on predefined skin color models. It is very difficult to predefine a color model in a mobile application where the light condition may change dramatically over time. In this paper, we propose a novel statistical approach to hand segmentation based on Bayes decision theory. The proposed method requires no predefined skin color model. Instead it generates a hand color model and a background color model for a given
more » ... e, and uses these models to classify each pixel in the image as either a hand pixel or a background pixel. Models are generated using a Gaussian mixture model with the restricted EM algorithm. Our method is capable of segmenting hands of arbitrary color in a complex scene. It performs well even when there is a significant overlap between hand and background colors, or when the user wears gloves. We show that the Bayes decision method is superior to a commonly used method by comparing their upper bound performance. Experimental results demonstrate the feasibility of the proposed method. (a © ) An example image, its hand and background portion (b) © Overall, hand and background histograms of (a) (c © ) Another example image ( © d)H istograms of( c)
doi:10.1109/afgr.2000.840673 dblp:conf/fgr/ZhuYW00 fatcat:23e5evehh5f6boqs6lrh47zqim