Re-sampling for Chinese Sign Language Recognition
In Sign Language recognition, one of the problems is to collect enough data. Data collection for both training and testing is a laborious but necessary step. Almost all of the statistical methods used in Sign Language Recognition suffer from this problem. Inspired by the crossover and mutation of genetic algorithms, this paper presents a method to enlarge Chinese Sign language database through re-sampling from existing sign samples. Two initial samples of the same sign are regarded as parents. They can reproduce their children by crossover. To verify the effectiveness of the proposed method, some experiments are carried out on a vocabulary with 350 static signs. Each sign has 4 samples. Three samples are used to be the original generation. These three original samples and their offspring are used to construct the training set, and the remaining sample is used for testing. The experimental results show that the data generated by the proposed method are effective.
KeywordsGesture Recognition Hand Gesture Sign Language Recognition Channel Number Hand Shape
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- 3.Sidney Fels, S.: Glove –TalkII: Mapping hand gestures to speech using neural networks-An approach to building adaptive interfaces. PhD thesis, Computer Science Department, University of Torono (1994)Google Scholar
- 4.Yanghee Nam, K.Y.W.: Recognition of space-time hand-gestures using hidden Markov model. In: ACM Symposium on Virtual Reality Software and Technology, HongKong, July 1996, pp. 51–58 (1996)Google Scholar
- 5.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)Google Scholar
- 6.Grobel, K., Assan, M.: Isolated sign language recognition using hidden Markov models. In: Proceedings of the International Conference of System, Man and Cybernetics, pp. 162–167 (1996)Google Scholar
- 7.Vogler, C., Metaxas, D.: Toward scalability in ASL Recognition: Breaking Down Signs into Phonemes. In: Proceedings of Gesture Workshop, Gif-sur-Yvette, France, pp. 400–404 (1999)Google Scholar
- 10.Vamplew, P., Adams, A.: Recognition of Sign Language Gestures Using Neural Networks. Australian Journal of Intelligent Information Processing Systems 5(2), 94–102 (Winter 1998)Google Scholar
- 11.Akyol, S., Canzler, U.: An information terminal using vision based sign language recognition. In: ITEA Workshop on Virtual Home Environments, pp. 61–68 (2002)Google Scholar
- 12.Chen, J., Chen, X., Gao, W.: Expand Training Set for Face Detection by GA Re-sampling. In: The 6th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2004), Seoul, Korea, May 17-19, pp. 73–79 (2004)Google Scholar