http://iet.metastore.ingenta.com
1887

Recognition of complex static hand gestures by using the wristband-based contour features

Recognition of complex static hand gestures by using the wristband-based contour features

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Recognition of complex static hand gestures is a challenging problem due to the complexity of hand gestures, which are rich in diversities because of high degrees of freedom involved by the human hand. It is more difficult especially when gestures are represented by two hands. This study proposes a framework that can recognise complex static hand gestures by using the wristband-based contour features (WBCFs). The authors require the user to wear a pair of black wristbands on his (her) two hand wrists, so that the hand region(s) can be segmented accurately. The topmost and sharpest corner point of the wristband on a gesturing hand is detected first. It is treated as a landmark to extract the WBCF of a hand gesture. Then, a simple feature matching method is proposed to obtain a recognition result. To deal with the cases where hand region(s) cannot be segmented correctly, watershed segmentation, and region merging techniques are adopted to provide improvements on hand region segmentation. Experimental results show that their system can be used to recognise 29 Turkish fingerspelling sign hand gestures and achieve a recognition accuracy of 99.31% with only six training images for each gesture.

References

    1. 1)
      • S. Mitra , T. Acharya .
        1. Mitra, S., Acharya, T.: ‘Gesture recognition: a survey’, IEEE Trans. Syst. Man Cybern. C, Appl. Rev., 2007, 37, (3), pp. 311324.
        . IEEE Trans. Syst. Man Cybern. C, Appl. Rev. , 3 , 311 - 324
    2. 2)
      • R.Z. Khan , N.A. Ibraheem .
        2. Khan, R.Z., Ibraheem, N.A.: ‘Survey on gesture recognition for hand image gestures’, Comput. Inf. Sci., 2012, 5, pp. 110121.
        . Comput. Inf. Sci. , 110 - 121
    3. 3)
      • A.R. Sarkar , G. Sanyal , S. Majumder .
        3. Sarkar, A.R., Sanyal, G., Majumder, S.: ‘Hand gesture recognition systems: a survey’, Int. J. Comput. Appl., 2013, 71, pp. 2537.
        . Int. J. Comput. Appl. , 25 - 37
    4. 4)
      • G. Plouffe , A. Cretu .
        4. Plouffe, G., Cretu, A.: ‘Static and dynamic hand gesture recognition in depth data using dynamic time warping’, IEEE Trans. Instrum. Meas., 2016, 65, (2), pp. 305316.
        . IEEE Trans. Instrum. Meas. , 2 , 305 - 316
    5. 5)
      • Y. Li .
        5. Li, Y.: ‘Multi-scenario gesture recognition using Kinect’. Proc. IEEE Int. Conf. Computer Games, July/August 2012, pp. 126130.
        . Proc. IEEE Int. Conf. Computer Games , 126 - 130
    6. 6)
      • O. Altun , S. Albayrak .
        6. Altun, O., Albayrak, S.: ‘Turkish fingerspelling recognition system using generalized Hough transform, interest regions, and local descriptors’, Pattern Recognit. Lett., 2011, 32, pp. 16261632.
        . Pattern Recognit. Lett. , 1626 - 1632
    7. 7)
      • Y. Zhao , J. Liu , H. Li .
        7. Zhao, Y., Liu, J., Li, H., et al: ‘Improved watershed algorithm for dowels image segmentation’. Proc. Seventh IEEE World Congress on Intelligent Control and Automation, 2008, pp. 76447648.
        . Proc. Seventh IEEE World Congress on Intelligent Control and Automation , 7644 - 7648
    8. 8)
      • A.R. Várkonyi-Kóczy , B. Tusor .
        8. Várkonyi-Kóczy, A.R., Tusor, B.: ‘Human–computer interaction for smart environment applications using fuzzy hand posture and gesture models’, IEEE Trans. Instrum. Meas., 2011, 60, (5), pp. 15051514.
        . IEEE Trans. Instrum. Meas. , 5 , 1505 - 1514
    9. 9)
      • C-C. Hsieh , D-H. Liou .
        9. Hsieh, C-C., Liou, D-H.: ‘Novel Haar features for real-time hand gesture recognition using SVM’, J. Real-Time Image Process., 2015, 10, (2), pp. 357370.
        . J. Real-Time Image Process. , 2 , 357 - 370
    10. 10)
      • M. Tang .
        10. Tang, M.: ‘Recognizing hand gestures with Microsoft's Kinect’, Stanfordedu, 2011, 14, (4), pp. 303313.
        . Stanfordedu , 4 , 303 - 313
    11. 11)
      • I. Dejmal , M. Zacksenhouse .
        11. Dejmal, I., Zacksenhouse, M.: ‘Coordinative structure of manipulative hand-movements facilitates their recognition’, IEEE. Trans. Biomed. Eng., 2006, 53, (12), pp. 24552463.
        . IEEE. Trans. Biomed. Eng. , 12 , 2455 - 2463
    12. 12)
      • L. Dipietro , A.M. Sabatini , P. Dario .
        12. Dipietro, L., Sabatini, A.M., Dario, P.: ‘Survey of glove based systems and their applications’, IEEE Trans. Syst. Cybern., 2008, 38, (4), pp. 461482.
        . IEEE Trans. Syst. Cybern. , 4 , 461 - 482
    13. 13)
      • J. Park , Y-L. Yoon .
        13. Park, J., Yoon, Y-L.: ‘Led-glove based interactions in multi-modal displays for teleconferencing’. Proc. Int. Conf. Artificial Reality Telexistence, December 2006, pp. 395399.
        . Proc. Int. Conf. Artificial Reality Telexistence , 395 - 399
    14. 14)
      • P.G. Kry , D.K. Pai .
        14. Kry, P.G., Pai, D.K.: ‘Interaction capture and synthesis’, ACM Trans. Graph., 2006, 25, (3), pp. 872880.
        . ACM Trans. Graph. , 3 , 872 - 880
    15. 15)
      • J. Han , L. Shao , J. Shotton .
        15. Han, J., Shao, L., Shotton, J.: ‘Enhanced computer vision with Microsoft Kinect sensor: a review’, IEEE Trans. Cybern., 2013, 43, (5), pp. 13181334.
        . IEEE Trans. Cybern. , 5 , 1318 - 1334
    16. 16)
      • C. Wang , Z. Liu , S. Chan .
        16. Wang, C., Liu, Z., Chan, S.: ‘Superpixel-based hand gesture recognition with Kinect depth camera’, IEEE Trans. Multimed., 2015, 17, (1), pp. 2939.
        . IEEE Trans. Multimed. , 1 , 29 - 39
    17. 17)
      • Y. Yao , Y. Fu .
        17. Yao, Y., Fu, Y.: ‘Contour model-based hand-gesture recognition using the Kinect sensor’, IEEE Trans. Circuits Syst. Video Technol., 2014, 24, (11), pp. 19351944.
        . IEEE Trans. Circuits Syst. Video Technol. , 11 , 1935 - 1944
    18. 18)
      • Q. Chen , N.D. Georganas , E.M. Petriu .
        18. Chen, Q., Georganas, N.D., Petriu, E.M.: ‘Hand gesture recognition using haarlike features and a stochastic context-free grammar’, IEEE Trans. Instrum. Meas., 2008, 57, (8), pp. 15621571.
        . IEEE Trans. Instrum. Meas. , 8 , 1562 - 1571
    19. 19)
      • F. Jiang , C. Wang , Y. Gao .
        19. Jiang, F., Wang, C., Gao, Y., et al: ‘Discriminating features learning in hand gesture classification’, IET Comput. Vis., 2015, 9, (5), pp. 673680.
        . IET Comput. Vis. , 5 , 673 - 680
    20. 20)
      • Z. Ren , J. Yuan .
        20. Ren, Z., Yuan, J.: ‘Robust part-based hand gesture recognition using Kinect sensor’, IEEE Trans. Multimedia, 2013, 15, (5), pp. 11101120.
        . IEEE Trans. Multimedia , 5 , 1110 - 1120
    21. 21)
      • M. Schroder , C. Elbrechter , J. Maycock .
        21. Schroder, M., Elbrechter, C., Maycock, J., et al: ‘Real-time hand tracking with a color glove for the actuation of anthropomorphic robot hands’. Proc. IEEE-RAS Int. Conf. Humanoid Robots, Osaka, Japan, November/December 2012, pp. 262269.
        . Proc. IEEE-RAS Int. Conf. Humanoid Robots , 262 - 269
    22. 22)
      • H. Takimoto , S. Yoshimori , Y. Mitsukura .
        22. Takimoto, H., Yoshimori, S., Mitsukura, Y., et al: ‘Classification of hand postures based on 3D vision model for human–robot interaction’. Proc. IEEE Int. Symp. Robot Human Interactive Communication, Viareggio, Italy, September 2010, pp. 292297.
        . Proc. IEEE Int. Symp. Robot Human Interactive Communication , 292 - 297
    23. 23)
      • N.H. Dardas , N.D. Georganas .
        23. Dardas, N.H., Georganas, N.D.: ‘Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques’, IEEE Trans. IM, 2011, 60, (11), pp. 35923607.
        . IEEE Trans. IM , 11 , 3592 - 3607
    24. 24)
      • C.-C. Wang , K.-C. Wang .
        24. Wang, C.-C., Wang, K.-C.: ‘Hand gesture recognition using AdaBoost with SIFT for human robot interaction’. Recent Progress in Robotics: LNCIS, 2008, vol. 370, pp. 317329.
        . Recent Progress in Robotics: LNCIS , 317 - 329
    25. 25)
      • F. Pedersoli , S. Benini , N. Adami .
        25. Pedersoli, F., Benini, S., Adami, N., et al: ‘XKin: an open source framework for hand pose and gesture recognition using Kinect’, Vis. Comput., 2014, 30, (10), pp. 11071122.
        . Vis. Comput. , 10 , 1107 - 1122
    26. 26)
      • M.B. Kaaniche , F. Bremond .
        26. Kaaniche, M.B., Bremond, F.: ‘Recognizing gestures by learning local motion signatures of HOG descriptors’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (11), pp. 22472258.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 11 , 2247 - 2258
    27. 27)
      • H. Wu , L. Wang , M. Song . (2014)
        27. Wu, H., Wang, L., Song, M., et al: ‘Automatic hand gesture recognition based on shape context’, in Wen, Z., Li, T. (EDs.): ‘Foundations of intelligent system, advances in intelligent systems and computing’, vol. 277 (Springer-Verlag, 2014), pp. 889900.
        .
    28. 28)
      • N.H. Dardas , E.M. Petriu .
        28. Dardas, N.H., Petriu, E.M.: ‘Hand gesture detection and recognition using principal component analysis’. Proc. CIMSA, Ottawa, Canada, 2011, pp. 16.
        . Proc. CIMSA , 1 - 6
    29. 29)
      • W.S. You .
        29. You, W.S.: ‘Hand posture recognition based on shape histogram and contour features’. Master thesis, Ming Chuan University, 2016.
        .
    30. 30)
      • P. Soille . (1999)
        30. Soille, P.: ‘Morphological image analysis: principles and applications’ (Springer-Verlag, 1999).
        .
    31. 31)
      • E. Rosten , T. Drummond .
        31. Rosten, E., Drummond, T.: ‘Machine learning for high-speed corner detection’. Proc. European Conf. Computer Vision, 2006, vol. 1, pp. 430443.
        . Proc. European Conf. Computer Vision , 430 - 443
    32. 32)
      • D.G. Lowe .
        32. Lowe, D.G.: ‘Distinctive image features from scale-invariant keypoints’, Int. J. Comput. Vis., 2004, 60, (2), pp. 91110.
        . Int. J. Comput. Vis. , 2 , 91 - 110
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2016.1139
Loading

Related content

content/journals/10.1049/iet-ipr.2016.1139
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address