An accurate hand segmentation approach using a structure based shape localization technique

Hand segmentation is an important stage for a variety of applications such as gesture recognition and biometrics. The accuracy of the hand segmentation process becomes more critical in applications that are based on hand measurements as in the case of biometrics. In this paper, we present a very accurate hand segmentation technique, relying on both hand localization and color information. First, our proposal locates a hand on an input image, the hand location is then used to extract a training region which will play a critical role for segmenting the whole hand in an accurate way. We use a structure-based method (STELA), originally proposed for 3D model retrieval, for the hand localization stage. STELA exploits not only locality but also structural information of the hand image and does not require a large image collection for training. Second, our proposal separates the hand region from the background using the color information captured from the training region. In this way, the segmentation depends only on the user skin color. This segmentation approach allows us to handle a variety of skin colors and illumination conditions. In addition, our proposal is characterized by being fully automatic, where a user calibration stage is not required. Our results show a 100% in the hand localization process under different kinds of images and a very accurate hand segmentation achieving over 90% of correct segmentation at the expense of having only 5% for false positives.