Chinese Sign Language Recognition Based on DTW-Distance-Mapping Features

Sign language is an important communication tool between the deaf and the external world. As the number of the Chinese deaf accounts for 15% of the world, it is highly urgent to develop a Chinese sign language recognition (CSLR) system. Recently, a novel phonology- and radical-coded CSL, taking advantages of a limited and constant number of coded gestures, has been preliminarily verified to be feasible for practical CSLR systems. The keynote of this version of CSL is that the same coded gesture performed in different orientations has different meanings. In this paper, we mainly propose a novel two-stage feature representation method to effectively characterize the CSL gestures. First, an orientation-sensitive feature is extracted regarding the distances between the palm center and the key points of the hand contour. Second, the extracted features are transformed by a dynamic time warping- (DTW-) based feature mapping approach for better representation. Experimental results demonstrate the effectiveness of the proposed feature extraction and mapping approaches. The averaged classification accuracy of all the 39 types of CSL gestures acquired from 11 subjects exceeds 93% for all the adopted classifiers, achieving significant improvement compared to the scheme without DTW-distance-mapping.