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Recognizing and Interpreting Sign Language Gesture for Human Robot Interaction

by Shekhar Singh, Akshat Jain, Deepak Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 11
Year of Publication: 2012
Authors: Shekhar Singh, Akshat Jain, Deepak Kumar
10.5120/8247-1758

Shekhar Singh, Akshat Jain, Deepak Kumar . Recognizing and Interpreting Sign Language Gesture for Human Robot Interaction. International Journal of Computer Applications. 52, 11 ( August 2012), 24-31. DOI=10.5120/8247-1758

@article{ 10.5120/8247-1758,
author = { Shekhar Singh, Akshat Jain, Deepak Kumar },
title = { Recognizing and Interpreting Sign Language Gesture for Human Robot Interaction },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 11 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 24-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number11/8247-1758/ },
doi = { 10.5120/8247-1758 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:51:59.964327+05:30
%A Shekhar Singh
%A Akshat Jain
%A Deepak Kumar
%T Recognizing and Interpreting Sign Language Gesture for Human Robot Interaction
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 11
%P 24-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Visual interpretation of sign language gesture can be useful in accomplishing natural human robot interaction. This paper describes a sign language gesture based recognition, interpreting and imitation learning system using Indian Sign Language for performing Human Robot Interaction in real time. It permits us to construct a convenient sign language gesture based communication with humanoid robot. The classification, recognition, learning, interpretation process is carried out by extracting the features from Indian sign language (ISL) gestures. Chain code and fisher score is considered as a feature vector for classification and recognition process. It is to be done by the two statistical approaches namely known as Hidden Markov Model (HMM) technique and feed forward back propagation neural network (FNN) in order to achieve satisfactory recognition accuracy. The sensitivity, specificity and accuracy were found to be equal 98. 60%, 97. 64% and 97. 52% respectively. It can be concluded that FNN gives fast and accurate recognition and it works as promising tool for recognition and interpretation of sign language gesture for human computer interaction. The overall accuracy of recognition and interpretation of the proposed system is 95. 34%. Thus, this approach is suitable for automated real time human computer interaction tool.

References
  1. Zhang, J. , Zhao, M. : A vision-based gesture recognition system for human-robot interaction. Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference on, vol. , no. , pp. 2096-2101, 19-23 Dec. (2009). doi: 10. 1109/ROBIO. 2009. 5420512
  2. Calinon, S. , Guenter, F. , Billard, A. : On Learning, Representing and Generalizing a Task in a Humanoid Robot. IEEE Trans. on Systems, Man and Cybernetics, Part B, Vol. 37, No. 2, pp. 286-298 (2007). doi: 10. 1109/TSMCB. 2006. 886952
  3. Calinon, S. , Guenter, F. , Billard, A. : Goal-Directed Imitation in a Humanoid Robot. International Conference on Robotics and Automation (ICRA), pp. 299-304 (2005).
  4. Pantic, M. , Rothkrantz, L. J. M. : Toward an affect-sensitive multimodal human-computer interaction. IEEE, vol. 91, no. 9, pp. 1370- 1390, Sept. (2003).
  5. Bhuyan, M. K. , Ghoah, D. , Bora, P. K. : A Framework for Hand Gesture Recognition with Applications to Sign Language. India Conference, 2006 Annual IEEE, PP. 1-6, Sept. (2006). doi: 10. 1109/INDCON. 2006. 302823
  6. Prasad, J. S. , Nandi, G. C. : Clustering Method Evaluation for Hidden Markov Model Based Real-Time Gesture Recognition. Advances in Recent Technologies in Communication and Computing, ARTCom '09, pp. 419-423, 27-28Oct. (2009).
  7. Lee, H. J. , Chung, J. H. : Hand gesture recognition using orientation histogram. TENCON 99. Proceedings of the IEEE Region 10 Conference, vol. 2, no. , pp. 1355-1358 vol. 2, Dec. (1999). doi: 10. 1109/TENCON. 1999. 818681
  8. Freeman, W. T. , Roth, M. : Orientation histograms for hand gesture recognition. Intl. Workshop on Automatic Face- and Gesture- Recognition, IEEE Computer Society, Zurich, Switzerland, pp. 296—301, June (1995). MERL-TR94-03.
  9. Nandy, A. , Prasad, J. S. , Chakraborty, P. , Nandi, G. C. , Mondal, S. : Classification of Indian Sign Language In Real Time. International Journal on Computer Engineering and Information Technology (IJCEIT), Vol. 10, No. 15, pp. 52-57, Feb. (2010).
  10. Nandy, A. , Prasad, J. S. , Mondal, S. , Chakraborty, P. , Nandi, G. C. : Recognition of Isolated Indian Sign Language gesture in Real Time. BAIP 2010, Springer LNCS-CCIS, Vol. 70, pp. 102-107, March (2010). doi: 10. 1007/978-3-642-12214-9_18.
  11. Dasgupta, T. , Shukla, S. , Kumar, S. , Diwakar, S. , Basu, A,: A Multilingual Multimedia Indian Sign Language Dictionary Tool. The 6'Th Workshop on Asian Language Resources, pp. 57-64 (2008).
  12. Kim, J. , Thang, N. D. , Kim, T. : 3-D hand motion tracking and gesture recognition using a data glove. Industrial Electronics, 2009. ISIE 2009. IEEE International Symposium on, vol. , no. , pp. 1013-1018, 5-8 July (2009). doi: 10. 1109/ISIE. 2009. 5221998
  13. Jiangqin, W. , Wen, G. , Yibo, S. , Wei, L. , Bo, P. : A simple sign language recognition system based on data glove. Signal Processing Proceedings, 1998. ICSP '98. 1998 Fourth International Conference on, vol. 2, no. , pp. 1257-1260 vol. 2 (1998). doi: 10. 1109/ICOSP. 1998. 770847
  14. Ishikawa, M. , Matsumura, H. : Recognition of a hand-gesture based on self-organization using a DataGlove. Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on, vol. 2, no. , pp. 739-745 vol. 2 (1999). doi: 10. 1109/ICONIP. 1999. 845688
  15. Swee, T. T. , Ariff, A. K. , Salleh, S. H. , Seng, S. K. , Huat, L. S. : Wireless data gloves Malay sign language recognition system. Information, Communications & Signal Processing, 2007 6th International Conference on, vol. , no. , pp. 1-4, 10-13 Dec. (2007). doi: 10. 1109/ICICS. 2007. 4449599
  16. Liang, R. H. , Ouhyoung, M. : A real-time continuous gesture recognition system for sign language. Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on, vol. , no. , pp. 558-567, 14-16 Apr (1998).
  17. Won, D. , Lee, H. G. , Kim, J. Y. , Choi, M. , Kang, M. S. : Development of a wearable input device based on human hand-motions recognition. Intelligent Robots and Systems, 2004. (IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference on, vol. 2, no. , pp. 1636- 1641 vol. 2, 28 Sept. -2 Oct. (2004). doi: 10. 1109/IROS. 2004. 1389630
  18. Kuzmanic, A. , Zanchi, V. : Hand shape classification using DTW and LCSS as similarity measures for vision-based gesture recognition system. EUROCON, 2007. The International Conference on "Computer as a Tool", vol. , no. , pp. 264-269, 9-12 Sept. (2007). doi: 10. 1109/EURCON. 2007. 4400350
  19. Hienz, H. , Grobel, K. , Offner, G. : Real-time hand-arm motion analysis using a single video camera. Automatic Face and Gesture Recognition, 1996. , Proceedings of the Second International Conference on , vol. , no. , pp. 323-327, 14-16 Oct. (1996).
  20. Hasanuzzaman, M. , Ampornaramveth, V. , Zhang, T. , Bhuiyan, M. A. , Shirai, Y. , Ueno, H. : Real-time Vision-based Gesture Recognition for Human Robot Interaction. Robotics and Biomimetics, 2004. ROBIO 2004. IEEE International Conference on, vol. , no. , pp. 413-418, 22-26 Aug. (2004). doi: 10. 1109/ROBIO. 2004. 1521814.
  21. Rabiner, L. R. : A tutorial on hidden Markov models and selected applications in speech recognition. IEEE, vol. 77, no. 2, pp. 257-286, Feb. (1989). doi: 10. 1109/5. 18626.
  22. Vector Quantization Technique and LBG Algorithm. www. cs. ucf. edu/courses/cap5015/vector. ppt.
  23. Michailovich, O. , Rathi, Y. , Tannenbaum, A. : Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow. IEEE Transactions on Image Processing, vol. 16, no. 11, pp. 2787-2801, Nov. (2007). doi: 10. 1109/TIP. 2007. 908073
  24. Kailath, T. : The Divergence and Bhattacharyya Distance Measures in Signal Selection. IEEE Transactions on Communication Technology, vol. 15, no. 1, pp. 52-60, Feb. (1967).
  25. Nayak, S. , Sarkar, S. , Loeding, B. : Distribution-Based Dimensionality Reduction Applied to Articulated Motion Recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 31, no. 5, pp. 795-810, May (2009). doi: 10. 1109/TPAMI. 2008. 80.
  26. Nandy, A. , Mondal, S. , Prasad, J. S. , Chakraborty, P. , Nandi, G. C. : Recognizing & interpreting Indian Sign Language gesture for Human Robot Interaction. Computer and Communication Technology (ICCCT), 2010 International Conference on , vol. , no. , pp. 712-717, 17-19 Sept. (2010). doi: 10. 1109/ICCCT. 2010. 5640434.
  27. Mitra, S. , Acharya, T. : Gesture Recognition: A Survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 37, no. 3, pp. 311-324, May (2007). doi: 10. 1109/TSMCC. 2007. 893280.
  28. Lawence R. Rabiner, "A tutorial on Hidden Markov models and Selected Applications in Speech Recognition", in Proceedings of the IEEE, vol. 77, no. 2,IEEE, pp. 257-286, 1989.
  29. L. O'Gorman and R. Kasturi, Document Image AnalJsis, IEEE Computer Society Press, 1995.
  30. J. Serra, Image Analysis and Mathematical Morphology, Academic Press, 1982.
  31. C. Travieso, C. Morales, I. Alonso y M. Ferrer, "Handwritten digits parameterisation for HMM based recognition", Proceedings of the Image Processing and its Applications,vol. 2, pp. 770-774, julio de 1999.
  32. E. Gomez, C. M. Travieso, J. C. Briceiio, M. A. Ferrer, "Biometric Identification Svstem by Lip Shape", in Proceeding of 361h International Carnahan Conference on Security Technology, Atlantic City, October 2002,pp. 39-42.
  33. L. Fausett, "Fundamentals of Neural Networks, Architectures, Algorithms, and Applications", Prentice-Hall, Inc. 1994, pp-304-315.
  34. K. Murakami, H. Taguchi: Gesture Recognition using Recurrent Neural Networks. In CHI '91 Conference Proceedings (pp. 237-242). ACM. 1991.
  35. Chang, J. Chen, W. Tai, and C. Han, ?New Approach for Static Gesture Recognition", Journal of Information Science and Engineering22, 1047-1057, 2006.
  36. S. Naidoo, C. Omlin and M. Glaser, "Vision-Based Static Hand Gesture Recognition Using Support Vector Machines", 1998. pages 88 – 94.
  37. Vladimir I. Pavlovic, Rajeev Sharma, Thomas S Huang, ?Visual Interpretation of Hand Gestures for Human-Computer Interaction: A review? IEEE Transactions of pattern analysis and machine intelligence, Vole 19, NO 7, July 1997, 677 - 695
  38. Visual gesture recognition. In proceedings IEEE Visual Image Signal Process, by Davis & Shah, 1994 vol. 141, Issue: 2, 101 – 106.
  39. Hand gesture Recognition of Radial Basis Functions (RBF) Networks and Decision Trees ?International Journal of Pattern Recognition and Artificial Intelligence. Volume: 11, Issue: 6(1997) pp. 845-850.
  40. Face detection in complicated backgrounds and different illumination conditions by using YCbCr color space and neural network Pattern Recognition Letters, Volume 28, and Issue 16, 1 December 2007, Pages 2190-2200.
Index Terms

Computer Science
Information Sciences

Keywords

HRI ISL CLAHE Chain Code HMM Fisher Score FNN Gesture recognizing Gesture Interpretation

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