Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG'00) Relevant Features for Video-Based Continuous Sign Language Recognition Grenoble, France9 March 26-March 30 ISBN: 0-7695-0580-5
This paper describes the development of a video-based continuous sign language recognition system. The system is based on continuous density Hidden Markov Models (HMM) with one model for each sign. Feature vectors reflecting manual sign parameters serve as input for training and recognition. To reduce computational complexity during the recognition task beam search is employed. The system aims for an automatic signer dependent recognition of sign language sentences, based on a lexicon of 97 sign of German Sign Language (GSL). A single color video camera is used for image recording. Furthermore the influence of different features reflecting different manual sign parameters on the recognition results are examined. Results are given for varying features and varying sized vocabulary. The system achieves accuracy of 91.7% based on a lexicon of 97 signs.
Index Terms:
sign language recognition, Hidden Markov Models, statistical pattern recognition, video-based human-computer interaction, feature extraction
Citation:
Britta Bauer, Hermann Hienz, "Relevant Features for Video-Based Continuous Sign Language Recognition," fg, pp.440, Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG'00), 2000 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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