Face and Hand Shape Segmentation Using Statistical Skin Detection for Sign Language Recognition

An accurate face and hand segmentation is the first and important step in sign language recognition systems. In this paper, we propose a method for face and hand segmentation that helps to build a better vision based sign language recognition system. The method proposed is based on YCbCr color space, single Gaussian model, Bayes rule and morphology operations. It detects regions of face and hands in complex background and non-uniform illumination. This method tested on 700 posture images of the sign language that are performed with one hand or both hands. Experimental results show that our method has achieved a good performance for images with complex background.

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