Hand and face segmentation using motion and color cues in digital image sequences

In this paper, we present a hand and face segmentation algorithm using motion and color cues. The algorithm is proposed for the content based representation of sign language image sequences, where the hands and face constitute a video object. Our hand and face segmentation algorithm consists of three stages, namely color segmentation, temporal segmentation, and video object plane generation. In color segmentation, we model the skin color as a normal distribution and classify each pixel as skin or non-skin based on its Mahalanobis distance. The aim of temporal segmentation is to localize moving objects in image sequences. A statistical variance test is employed to detect object motion between two consecutive images. Finally, the results from color and temporal segmentation are analyzed to yield a change detection mask. The performance of the algorithm is illustrated by simulation carried out on the silent test sequence.