Abstract
To collect data for sign language recognition is not a trivial task. The lack of training data has become a bottleneck in the research of singer independence and large vocabulary recognition. A novel sign language generation algorithm is introduced in this paper. The difference between signers is analyzed briefly and a criterion is introduced to distinguish the same gesture words of different signers. Basing on that criterion we propose a sign word generation method combining the static gesture quantization and Discrete Cosine Transform (DCT), which can generate the new signers’ sign words according to the existed signers’ sign words. The experimental result shows that not only the data generated are distinct with the training data, they are also demonstrated effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Charayaphan, C., Marble, A.: Image processing system for interpreting motion in American Sign Language. Journal of Biomedical Engineering 14(15), 419–425 (1992)
Fels, S.S., Hinton, G.: Glove Talk: A neural network interface between a DataGlove and a speech synthesizaer. IEEE Transactions on Neural Networks 4, 2–8 (1993)
Kadous, M.W.: Machine recognition of Auslan signs using PowerGlove: Towards large-lexicon recognition of sign language. In: proceeding of workshop on the Integration of Gesture in Language and Speech, Wilmington, DE, pp. 165–174 (1996)
Liang, R.H., Ouhyoung, M.: A Real-time Continuous Gesture Recognition System for Sign Language. In: Proceeding of the Third International Conference on Automatic Face and Gesture Recognition, Nara, Japan, pp. 558–565 (1998)
Vogler, C., Metaxas, D.: Toward Scalability in ASL Recognition: Breaking Down Signs into Phonemes. In: Braffort, A., Gibet, S., Teil, D., Gherbi, R., Richardson, J. (eds.) GW 1999. LNCS (LNAI), vol. 1739, pp. 400–404. Springer, Heidelberg (2000)
Jiangqin, W., Wen, G.: Sign Language Recognition Method on ANN/HMM. Computer science and application 9, 1–5 (1999)
Jiang-Qin, W., Wen, G.: A Hierarchical DGMM Recognizer for Chinese Sign Language Recognition. Journal of Software 11(11), 552–561 (2000)
Fang, G., Gao, W., Ma, J.: Signer-Independent Sign Language Recognition Based on SOFM/HMM. In: IEEE ICCV Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-time Systems (RATFG-RTS 2001), Vancouver, Canada, pp. 90–95 (2001)
Jiang, F., Yao, H., Yao, G.: Multilayer Architecture in Sign Language Recognition. In: Proceedings of the 5th International Conference on Multimodal Interfaces, pp. 102–104 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, C., Jiang, F., Yao, H., Yao, G., Gao, W. (2005). Static Gesture Quantization and DCT Based Sign Language Generation. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_22
Download citation
DOI: https://doi.org/10.1007/11573548_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29621-8
Online ISBN: 978-3-540-32273-3
eBook Packages: Computer ScienceComputer Science (R0)