Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Two-Stage Neural Networks for Sign Language Pattern Recognition

수화 패턴 인식을 위한 2단계 신경망 모델

  • 김호준 (한동대학교 전산전자공학부)
  • Received : 2012.04.02
  • Accepted : 2012.06.06
  • Published : 2012.06.25
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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.

본 논문에서는 착용식 추적장치나 표식 등의 보조 도구를 사용하지 않는 환경의 동영상 데이터로부터 수화 패턴을 인식하는 방법론에 관하여 고찰한다. 시스템 설계 및 구현에 관한 주제로서 특징점의 추출기법, 특징데이터의 표현기법 및 패턴 분류기법에 관한 방법론을 제시하고 그 유용성을 고찰한다. 일련의 동영상으로 표현되는 수화패턴에 대하여 특징점의 공간적 위치에 대한 변이 뿐만 아니라 시간차원의 변화를 고려한 특징데이터의 표현방법을 제시하며, 방대한 데이터에 의한 분류기의 크기 문제와 계산량의 문제를 개선하기 위하여 효과적으로 특징수를 줄일 수 있는 특징추출 방법을 소개한다. 패턴 분류과정에서 점진적 학습(incremental learning)이 가능한 신경망 모델을 제시하고 그 동작특성 및 학습효과를 분석한다. 또한 학습된 분류모델로부터 특징과 패턴 클래스 간의 상대적 연관성 척도를 정의하고, 이로부터 효과적인 특징을 선별하여 성능저하 없이 분류기의 규모를 최적화 할 수 있음을 보인다. 제안된 내용에 대하여 여섯 가지 수화패턴을 대상으로 적용한 실험을 통하여 유용성을 평가한다.

Keywords

References

  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 https://doi.org/10.1109/TPAMI.2005.112
  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 https://doi.org/10.1016/j.patrec.2010.11.013
  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. https://doi.org/10.1016/j.cviu.2008.09.005
  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. https://doi.org/10.1109/TSMCA.2007.904579
  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. https://doi.org/10.1109/TSMCB.2005.844594
  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. https://doi.org/10.1109/TPAMI.2002.1023803
  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. https://doi.org/10.1023/A:1020350100748
  8. Hung-Ming Sun, "Skin Detection for Single Images using Dynamic Skin Color Modeling," Pattern Recognition, Vol.43, pp.1413-1420, 2010. https://doi.org/10.1016/j.patcog.2009.09.022
  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. https://doi.org/10.1109/TSMCA.2010.2043948
  10. Patrick K. Simpson, "Fuzzy Min-Max Neural Network- Part1 : Classification." IEEE Transaction on Neural Network, Vol.3, No.5, pp.776-786, 1992. https://doi.org/10.1109/72.159066
  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. https://doi.org/10.1109/72.846747
  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 https://doi.org/10.1109/TPAMI.2004.97
  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.