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http://dx.doi.org/10.9708/jksci.2014.19.11.063

Sign Language Recognition System Using SVM and Depth Camera  

Kim, Ki-Sang (School of Media, Soongsil University)
Choi, Hyung-Il (School of Media, Soongsil University)
Publication Information
Journal of the Korea Society of Computer and Information / v.19, no.11, 2014 , pp. 63-72 More about this Journal
Abstract
In this paper, we propose a sign language recognition system using SVM and depth camera. Especially, we focus on the Korean sign language. For the sign language system, we suggest two methods, one in hand feature extraction stage and the other in recognition stage. Hand features are consisted of the number of fingers, finger length, radius of palm, and direction of the hand. To extract hand features, we use Distance Transform and make hand skeleton. This method is more accurate than a traditional method which uses contours. To recognize hand posture, we develop the decision tree with the hand features. For more accuracy, we use SVM to determine the threshold value in the decision tree. In the experimental results, we show that the suggested method is more accurate and faster when extracting hand features a recognizing hand postures.
Keywords
Sign Language; Hand Posture Recognition; SVM;
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1 Zaki, Mahmoud M., and Samir I. Shaheen. "Sign language recognition using a combination of new vision based features." Pattern Recognition Letters, Vol. 32, No. 4 pp. 572-577, March 2011.   DOI   ScienceOn
2 Yang, Ruiduo, and Sudeep Sarkar. "Coupled grouping and matching for sign and gesture recognition." Computer Vision and Image Understanding, Vol. 113, No. 6, pp. 663-681, June 2009.   DOI   ScienceOn
3 Juang, Chia-Feng, Shih-Hsuan Chiu, and Shen-Jie Shiu. "Fuzzy system learned through fuzzy clustering and support vector machine for human skin color segmentation." IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, Vol. 37, No.6, pp. 1077-1087, November 2007.   DOI   ScienceOn
4 Nolker, Claudia, and Helge Ritter. "Visual recognition of continuous hand postures." IEEE Transactions on Neural Networks, Vol. 13, No. 4 pp. 983-994, July 2002.   DOI   ScienceOn
5 Juang, Chia-Feng, and Ksuan-Chun Ku. "A recurrent fuzzy network for fuzzy temporal sequence processing and gesture recognition." IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 35, No. 4 pp. 646-658, August 2005.
6 Yang, Ming-Hsuan, Narendra Ahuja, and Mark Tabb. "Extraction of 2d motion trajectories and its application to hand gesture recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 8 pp. 1061-1074, August 2002.   DOI   ScienceOn
7 Fang, Yikai, et al. "A real-time hand gesture recognition method." IEEE International Conference on Multimedia and Expo, pp. 995-998, July 2007.
8 Suryanarayan, Poonam, Anbumani Subramanian, and Dinesh Mandalapu. "Dynamic hand pose recognition using depth data." Pattern Recognition (ICPR), 20th International Conference on. IEEE, pp. 3105-3108, August 2010.
9 Ren, Zhou, Junsong Yuan, and Zhengyou Zhang. "Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera." Proceedings of the 19th ACM international conference on Multimedia. ACM, pp. 1093-1096, November 2011.
10 Zabulis, Xenophon, Haris Baltzakis, and Antonis Argyros. "Vision-based hand gesture recognition for human-computer interaction." The Universal Access Handbook. LEA, pp. 1-56, June 2009.
11 Zafrulla, Zahoor, et al. "A novel approach to american sign language (asl) phrase verification using reversed signing." IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 48-55, June 2010.