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Development of a Hand Gesture Based Control Interface Using Deep Learning

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1070)


This paper describes the implementation of a control system based on ten different hand gestures, providing a useful approach for the implementation of better user-friendly human-machine interfaces. Hand detection is achieved using fast detection and tracking algorithms, and classification by a light convolutional neural network. The experimental results show a real-time response with an accuracy of 95.09%, and making use of low power consumption. These results demonstrate that the proposed system could be applied in a large range of applications such as virtual reality, robotics, autonomous driving systems, human-machine interfaces, augmented reality among others.


  • Gesture recognition
  • Human-machine interface
  • Deep learning
  • Real-time
  • Hand poses

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  • DOI: 10.1007/978-3-030-46140-9_14
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  1. Oyedotun, O.K., Khashman, A.: Deep learning in vision-based static hand gesture recognition. Neural Comput. Appl. 28(12), 3941–3951 (2016).

    CrossRef  Google Scholar 

  2. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)

    Google Scholar 

  3. Kwolek, B.: Face detection using convolutional neural networks and gabor filters. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 551–556. Springer, Heidelberg (2005).

    CrossRef  Google Scholar 

  4. Arel, I., Rose, D., Karnowski, T.: Research frontier: deep machine learning-a new frontier in artificial intelligence research. Comp. Intell. Mag. 5(4), 13–18 (2010)

    CrossRef  Google Scholar 

  5. Tompson, J., Stein, M., Lecun, Y., Perlin, K.: Real-time continuous pose recovery of human hands using convolutional networks. ACM Trans. Graph. 33(5), 1–10 (2014)

    CrossRef  Google Scholar 

  6. Nagi, J., Ducatelle, F., et al.: Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: IEEE ICSIP, pp. 342–347 (2011)

    Google Scholar 

  7. Barros, P., Magg, S., Weber, C., Wermter, S.: A multichannel convolutional neural network for hand posture recognition. In: Wermter, S., Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., Palm, G., Villa, A.E.P. (eds.) ICANN 2014. LNCS, vol. 8681, pp. 403–410. Springer, Cham (2014).

    CrossRef  Google Scholar 

  8. Koller, O., Ney, H., Bowden, R.: Deep hand: How to train a CNN on 1 million hand images when your data is continuous and weakly labelled. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3793–3802 (2016)

    Google Scholar 

  9. Yuan, S., Ye, Q., Stenger, B., Jain, S., Kim, T.: BigHand2.2M benchmark: hand pose dataset and state of the art analysis. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 2605–2613 (2017)

    Google Scholar 

  10. Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, pp. 2544–2550 (2010)

    Google Scholar 

  11. Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. Int. J. Comput. Vision 46(1), 81–96 (2002)

    CrossRef  Google Scholar 

  12. Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA (2015)

    Google Scholar 

  13. Li, D., Chen, X., Becchi, M., Zong, Z.: Evaluating the energy efficiency of deep convolutional neural networks on CPUs and GPUs. In: 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), Atlanta, GA, pp. 477–484 (2016)

    Google Scholar 

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Correspondence to Dennis Núñez-Fernández .

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Núñez-Fernández, D. (2020). Development of a Hand Gesture Based Control Interface Using Deep Learning. In: Lossio-Ventura, J.A., Condori-Fernandez, N., Valverde-Rebaza, J.C. (eds) Information Management and Big Data. SIMBig 2019. Communications in Computer and Information Science, vol 1070. Springer, Cham.

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  • Print ISBN: 978-3-030-46139-3

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