A vision-based sign language recognition system using tied-mixture density HMM

ICMI'04 - Sixth International Conference on Multimodal Interfaces

Volumn , Issue , 2004, Pages 198-204

  Zhang, Liang Guo ^a,c   Chen, Yiqiang ^a   Fang, Gaolin ^b   Chen, Xilin ^b   Gao, Wen ^a,b,c  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b HARBIN INSTITUTE OF TECHNOLOGY   (China)

^c UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Computer vision; Hidden Markov models; Human computer interaction; Sign language recognition

Indexed keywords

COMPUTER VISION; HUMAN COMPUTER INTERACTION; HUMAN ENGINEERING; MARKOV PROCESSES; MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS; USER INTERFACES;

AMERICAN SIGN LANGUAGE (ASL); HIDDEN MARKOV MODELS (HMM); SIGN LANGUAGE RECOGNITION (SLR); VIDEO-BASED SYSTEMS;

CHARACTER RECOGNITION;

EID: 14944358994     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1027933.1027967     Document Type: Conference Paper

References (17)

1. R. H. Liang, and M. Ouhyoung, A real-time continuous gesture recognition system for sign language, Proc. 3rd Int'l Conf. Automatic Face and Gesture Recognition, Nara, pp.558-565, 1998.
2. M.W. Kadous, Machine recognition of Auslan signs using PowerGloves: towards large-lexicon recognition of sign language, Proc. Workshop on the Integration of Gesture in Language and Speech, pp. 165-174, 1996.
3. S.S. Fels and G.E. Hinton, Glove-talk: A neural network interface between a data-glove and a speech synthesizer, IEEE Trans. Neural Networks, vol. 4, no. 1, pp. 2-8, 1993.
4. J.S. Kim, W. Jang, and Z. Bien, A dynamic gesture recognition system for the Korean sign language (KSL), IEEE Trans. Systems, Man, and Cybernetics, vol. 26, no. 2, pp. 354-359, 1996.
5. W. Gao, J.Y. Ma, J.Q. Wu, and C.L. Wang, Sign language recognition based on HMM/ANN/DP, Int'l J. Pattern Recognition and Artificial Intelligence, vol. 14, no. 5, pp. 587-602, 2000.
6. H. Mastuo, S. Igi, S. Lu, Y. Nagashima, Y. Takata, and T. Teshima, The recognition algorithm with non-contact for Japanese sign language using morphological analysis, Proc. Int'l Gesture Workshop, pp. 273-284, 1997.
7. T. Starner, J. Weaver, and A. Pentland, Real-time American sign language recognition using desk and wearable computer based video, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 12, pp. 1371-1375, 1998.
8. C. Vogler and D. Metaxas, Adapting hidden Markov models for ASL recognition by using three-dimensional computer vision methods, Proc. IEEE Int'l Conf. Systems, Man and Cybernetics, Orlando, pp. 156-161, 1997.
9. C. Vogler and D. Metaxas, Toward scalability in ASL recognition: breaking down signs into phonemes, Proc. Int'l Gesture Workshop, Gif-sur-Yvette, France, pp. 400-404, 1999.
10. C. Vogler and D. Metaxas, A framework for recognizing the simultaneous aspects of American sign language, Computer Vision and Image Understanding, vol. 81, no. 3, pp. 358-384, 2001.
11. K. Grobel and M. Assan, Isolated sign language recognition using hidden Markov models, Proc. IEEE Int'l Conf. Systems, Man and Cybernetics, pp. 162-167, 1996.
12. B. Bauer and H. Hienz, Relevant features for video-based continuous sign language recognition, Proc. 4th Int'l Conf. Automatic Face and Gesture Recognition, Grenoble, pp. 440-445, 2000.
13. L. R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, vol. 77, no. 2, pp. 257-285, 1989.
14. V. V. Digalakis, P. Monaco and H. Murveit, Genones: generalized mixture tying in continuous hidden Markov model-based speech recognizers. IEEE Trans. Speech and Audio Processing, vol 4, no. 4, pp. 281-289, 1996.
15. J. Miao, W. Gao et al, Gravity-Center Template Based Human Face Feature Detection, Proc. 3rd Int'l Conf. Multimodal Interface, Beijing, pp. 207-214, 2000.
16. S. Jabri, Z. Duric, H. Wechsler, and A. Rosenfeld, Detection and location of people in video images using adaptive fusion of color and edge information, Proc. Int'l Conf. Pattern Recognition, Barcelona, Spain, vol. 4, pp. 627-630, 2000.
17. C. Wang and W. Gao, The application of HMMs consisting of different number states in CSL recognition, Journal of Computer Research and Development, vol. 38, no. 1, pp.111-115, 2001 (In Chinese).