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Single-frame hand gesture recognition using color and depth kernel descriptors

This paper presents a flexible method for single-frame hand gesture recognition by fusing information from color and depth images. Existing methods usually focus on designing intuitive features for color and depth images. On the contrary, our method first extracts common patch-level features, and fuses them by means of kernel descriptors. Linear SVM is then adopted to predict the class label efficiently. In our experiments on two American Sign Language (ASL) datasets, we demonstrate that our approach recognizes each sign accurately with only a small number of training samples, and is robust to the change of distance between the hand and the camera.