VISION BASED MULTI-FEATURE HAND GESTURE RECOGNITION FOR INDIAN SIGN LANGUAGE MANUAL SIGNS

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

10
Reader(s)
22
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 9 , ISSUE 1 (March 2016) > List of articles

VISION BASED MULTI-FEATURE HAND GESTURE RECOGNITION FOR INDIAN SIGN LANGUAGE MANUAL SIGNS

Gajanan K. Kharate * / Archana S. Ghotkar *

Keywords : Indian sign language interpretation, k-nearest neighborhood classifier, nearest mean Classifier, Boundary moments, Fourier descriptor, 7 Hu moments, Chain code, Shape matrix, Naïve Bayes.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 9, Issue 1, Pages 124-147, DOI: https://doi.org/10.21307/ijssis-2017-863

License : (CC BY-NC-ND 4.0)

Received Date : 04-November-2015 / Accepted: 04-January-2016 / Published Online: 27-December-2017

ARTICLE

ABSTRACT

Indian sign language (ISL) is the main communication medium among deaf Indians. An ISL vocabulary show that the hand plays a significant role in ISL. ISL includes static and dynamic hand gesture recognition. The main aim of this paper is to present multi-feature static hand gesture recognition for alphabets and numbers. Here, comparative analysis of various feature descriptors such as chain code, shape matrix, Fourier descriptor, 7 Hu moments, and boundary moments is done. Multi-feature fusion descriptor is designed using contour (Boundary moments, Fourier descriptor) and region based (7Hu moments) descriptors. Analysis of this new multi-feature descriptor is done in comparison with other individual descriptors and it showed noteworthy results over other descriptors. Three classification methods such as, Nearest Mean Classifier (NMC), k-Nearest Neighborhood (k-NN) and Naive Bayes classifier are used for classification and comparison. New Multi-feature fusion descriptor shows high recognition rate of 99.61% among all with k-NN. Real time recognition for number signs 0-9, of fusion descriptor with NMC gave 100% accuracy.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[1] U. Zeshan, M. Vasistha and Sethna, “Implementation of Indian Sign Language in
Educational Setting,” Asia pacific Disability Rehabilitation Journal ,vol. 16, no. 1,
2001, pp. 16-39.
[2] C. Vogler, D. Metaxas, “A Framework for Recognizing the Simultaneous Aspect
of American Sign Language”, Computer Vision and Image Understanding, 81, 1998,
pp. 358-384.
[3] T. Starner, J. Weaver, A. Pentland, “Real-time American Sign Language
recognition using desk and wearable computer based video,” IEEE Transaction on
Pattern Analysis and Machine Intelligent, 20, 2010, pp. 1371-1375.
[4] M. Flasinski, S. Myslinski , “On the Use of Graph parsing for recognition
Of isolated hand posture of Polish Sign Language”, Pattern Recognition, Elsevier, 43,
2004, pp. 2249-2264.
[5] W. Gao, G. Fang, D. Zhao, “A Chinese sign language recognition system
based
on SOFM/SRN/HMM”, Pattern Recognition. Elsevier, vol. 37, 2013, pp. 2389-2402.
[6] J. Pansare et al., “Real-Time Static Devnagri sign Language Translation using
Histogram,” International Journal of Advanced Research in Computer Engineering &
Technology (IJARCET),vol. 2, no. 4, 2011, pp. 1455-1459.
[7] P. Subha, G. Balakrishnan , “Recognition of Tamil Sign Language Alphabet using
Image Processing to aid Deaf-Dumb People”, International Conference on
Communication Technology and System Design, vol. 1, 2012, pp. 861-868.
[8] M. Geetha, U. Manjusha , “A Vision based Recognition of Indian Sign
Language Alphabets and Numerals using B-Spline Approximation,” International
Journal on Computer Science and Engineering, vol. 4, no. 3, 2013, pp. 406-415.
[9] J. Singha, K. Das, “Recognition of Indian Sign Language in Live Video”,
International Journal of Computer Applications, vol. 70, no. 19, 2012, pp. 17-22.
[10] D. Tewari, S. Kumar, “A Visual Recognition of Static Hand Gesture in Indian
Sign Language based on Kohonen Self organizing Map Algorithm”, International
Journal of Engineering and Advanced Technology , vol. 2, no. 2, 2004, pp. 165-170.
[11] D. Zhang, C. Lu, “The Journal of The Pattern Recognition Society, Elsevier, vol.
37, no.1,2014,1-19.
[12] H. Cooper, B. Holt, R. Browden, Sign language recognition.
http://epubs.surrey.ac.uk/531441/1/SLR-LAP.pdf, accessed in sept-2014.
[13] Z. Huang, J. Leng, “Analysis of Hu Moment Invariants on Image Scaling and
Rotation”, Second International Conference on Computer Engineering and
Technology ,vol. 1, 2007, pp.476-480.
[14] S. Conseil, S. Bourennane, L. Martin, “Comparison Of Fourier Descriptors And
Hu Moments for Hand Posture Recognition”, European Signal Processing
Conference, (EUSIPCO),vol.1, 2009, pp. 1-6.
[15] J. Ravikiran et al., “Finger detection for sign language detection”, Proceedings of
the International Multi-Conference of Engineers and Computer Scientists , Hong
Kong ,vol. 1, 2011, pp. 1-5.
[16] J. Rekha, J. Bhattacharya and S. Majumder , “Hand Gesture Recognition for Sign
Language: A New Hybrid Approach”, 15th International Conference on Image
Processing, Computer Vision & Pattern Recognition IPCV‟11, CSREA Press, Las
Vegas, Nevada, USA, vol.1, 2000, pp. 30-35.
[17] A. Jain , R. Duin, “Statistical Pattern Recognition: A Review”, IEEE
Transactions On Pattern Analysis And Machine Intelligence, vol. 22, no. 1, 2009, pp.
4-37.
[18] J. Li, B. Lu, “An adaptive image Euclidean Distance”, Elsevier, Vol. 42, no. 3,
2009, pp.349-357.
[19] S. Cha, “Comprehensive Survey on Distance/Similarity Measures between
Probability Density Function”, International Journal of Mathematical Models and
Methods in Applied Science, vol. 1, no. 4, 2007, pp. 300-307.
[20] K. Wong,R. Cipollas, “Continuous gesture recognition using a sparse Bayesian
classifier”, International conference on pattern recognition, 2009, pp. 1084-87.
[21] T. Maung, “Real-Time Hand Tracking and Gesture Recognition System
Using Neural Networks,” Proceedings of World Academy of Science, Engineering
and Technology, vol. 3, 2007, pp. 470-478.
[22] Q. Munib, Moussa, Bayean, Hiba, “ American Sign Language(ASL) recognition
based on Hough transform and neural network”, Expert Systems with Applications,
Elsevier,2002, pp. 24-37.
[23] A. Corradini, “Real-Time Gesture Recognition by means of Hybrid Recognizers,”
Gesture Workshop, LNAI 2298, Springer-Verlag Berlin Heidelberg, 2014, pp. 34-47.
[24] ISL Dictionary: http://indiansignlanguage.org/isl-dictionary, accessed May 2014.
[25] S. Phung, A. Bouzerdoum, D. Chai, “Skin Segmentation Using Colour Pixel
Classification: Analysis and Comparison”, IEEE Transaction on Pattern Analysis and
Machine Intelligence, vol. 27, no. 10, 2005, pp. 148-154.
[26] A. Elgammal, C. Muang, D. Hu, D. Chai , “Skin Detection a Short Tutorial” ,
Encyclopedia of Biometrics, Springer-Verlag Berlin Heidelberg, 2002.
[27] M. Jones, J. Rehg, “Statistical Color Models with Application to Skin Detection”,
International Journal of Computer Vision, vol. 46, no. 1, 2002, pp. 81-96.
[28] A. S. Ghotkar , G. K. Kharate, “Hand Segmentation Techniques to Hand
Gesture Recognition for Natural Human Computer Interaction”, International Journal of Human Computer Interaction(IJHCI),Computer Science Journal,
Malaysia, vol. 3, no. 1, 2012, pp. 15-25.
[29] Archana S. Ghotkar, Gajanan K. Kharate, “Vision based Real time Hand Gesture
Recognition Techniques for HCI ”, International Journal of computer Application,
Volume. 70, No. 16, Foundation of Computer Science, New York, USA, 2013, pp.1-6.
[30] T. Dasgupta, S. Shulka, S. Kumar, S. Diwakar, A. Basu, “A Multilingual
Multimedia Indian Sign Language Dictionary Tool,” Proceedings 6th Workshop on
Asian Language Resources, vol.1, 2008, pp. 57-64.
[31] C. Harshith et al., “Survey on Various Gesture Recognition Techniques for
Interfacing Machine Based on Ambient Intelligence”, International Journal of
Computer Science and Engineering Survey, 2011, vol. 1, no. 2, pp. 31-33.
[32] Archana S. Ghotkar and Gajanan K. Kharate, “Study of Hand Gesture Recognition
for Indian Sign Language”, International Journal of Smart Sensing and Intelligent
Systems, Vol. 7, No.1, 2014, pp. 96-115.
[33] H. Lilha, D. Shivmurthy, “Evaluation of Features for Automated Transcription of
Dual-Handed Sign Language Alphabet”, Proceedings of International Conference on
Image Information Processing, 2013, pp. 1-5.
[34] S. Goyal, I. Sharma, S. Sharma, “Sign Language Recognition System for Deaf
and Dumb People”, International Journal on Engineering and Research Technology,
vol. 2, no. 4, 2013, pp. 382-387.
[35] Tom Fawcett, “An introduction to ROC analysis”, Pattern recognition letters,
Elsevier,vol.27,2006,pp.861-874.

EXTRA FILES

COMMENTS