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http://dx.doi.org/10.5391/JKIIS.2012.22.3.319

Two-Stage Neural Networks for Sign Language Pattern Recognition  

Kim, Ho-Joon (한동대학교 전산전자공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.22, no.3, 2012 , pp. 319-327 More about this Journal
Abstract
In this paper, we present a sign language recognition model which does not use any wearable devices for object tracking. The system design issues and implementation issues such as data representation, feature extraction and pattern classification methods are discussed. The proposed data representation method for sign language patterns is robust for spatio-temporal variances of feature points. We present a feature extraction technique which can improve the computation speed by reducing the amount of feature data. A neural network model which is capable of incremental learning is described and the behaviors and learning algorithm of the model are introduced. We have defined a measure which reflects the relevance between the feature values and the pattern classes. The measure makes it possible to select more effective features without any degradation of performance. Through the experiments using six types of sign language patterns, the proposed model is evaluated empirically.
Keywords
sign language recognition; neural network; feature extraction; pattern classification;
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1 Sylvie C.W. Ong and Surendra Ranganath, "Automatic Sign Language Analysis: A Survey and Future beyond Lexical Meaning," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 27, No.6, pp.873-891, 2005   DOI   ScienceOn
2 Mahmoud M. Zaki 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, 2011   DOI
3 Ruiduo Yang, Sudeep Sarkar, "Coupled Grouping and Matching for Sign and Gesture Recognition," Computer Vision and Image Understanding Vol.113, pp.663-581, 2009.   DOI   ScienceOn
4 Chia-Feng Juang, Shih-Hsuan Chiu, and Shen-Jie Shiu, "Fuzzy System Learned Through Fuzzy Clustering and Support Vector Machine for Human Skin Color Segmentation," IEEE Transaction on System, Man, and Cybernetics-Part A: Systems and Humans. Vol.37, No.6, pp.1077-1087, 2007.   DOI
5 Chia-Feng Juang and Ksuan-Chun Ku, "A Recurrent Fuzzy Network for Fuzzy Temporal Sequence Processing and Gesture Recognition," IEEE Transaction on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol.35, No.4, pp.646-658, 2005.   DOI
6 Ming-Hsuan Yang, Narendra Ahuja, and Mark Tabb, "Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.24, No.8, pp.1061-1074, 2002.   DOI   ScienceOn
7 Cen Rao, Alper Yilmaz and Mubarak Shah, " View-Invariant Representation and Recognition of Actions." International Journal of Computer Vision, Vol.50, No.2, pp.203-226, 2002.   DOI
8 Hung-Ming Sun, "Skin Detection for Single Images using Dynamic Skin Color Modeling," Pattern Recognition, Vol.43, pp.1413-1420, 2010.   DOI
9 Anas Wuteishat, Chee Peng Lim, and Kay Sin Tan, "A Modified Fuzzy Min-Max Neural Network With A Genetic-Algorithm-Based Rule Extractor for Pattern Classification," IEEE Transaction on System, Man, and Cybernetics-Part A: Systems and Humans. Vol.40, No.3, pp.641-650, 2010.   DOI
10 Patrick K. Simpson, "Fuzzy Min-Max Neural Network- Part1 : Classification." IEEE Transaction on Neural Network, Vol.3, No.5, pp.776-786, 1992.   DOI   ScienceOn
11 B. Gabrys, A. Bargiela,"General Fuzzy Min-Max Neural Network for Clustering and Classification," IEEE Transaction on Neural Networks, Vo.11, No.3, pp.769-783, 2000.   DOI
12 Cristophe Garcia, Manolis Delakis: Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No.11, 1408-1423, 2004   DOI
13 Ho-Joon Kim, Juho Lee, Hyun-Seung Yang, "A Weighted FMM Network and Its Application to Face Detection," Lecture Notes in Computer Science, Vol. 4233. pp. 177-186, 2006.