Smoothed Disparity Maps for Continuous American Sign Language Recognition

For the recognition of continuous sign language we analyse whether we can improve the results by explicitly incorporating depth information. Accurate hand tracking for sign language recognition is made difficult by abrupt and fast changes in hand position and configuration, overlapping hands, or a hand signing in front of the face. In our system depth information is extracted using a stereo-vision method that considers the time axis by using preand succeeding frames. We demonstrate that depth information helps to disambiguate overlapping hands and thus to improve the tracking of the hands. However, the improved tracking has little influence on the final recognition results.