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A Two-Stream CNN Framework for American Sign Language Recognition Based on Multimodal Data Fusion

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Advances in Computational Intelligence Systems (UKCI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1043))

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Abstract

At present, vision-based hand gesture recognition is very important in human-robot interaction (HRI). This non-contact method enables natural and friendly interaction between people and robots. Aiming at this technology, a two-stream CNN framework (2S-CNN) is proposed to recognize the American sign language (ASL) hand gestures based on multimodal (RGB and depth) data fusion. Firstly, the hand gesture data is enhanced to remove the influence of background and noise. Secondly, hand gesture RGB and depth features are extracted for hand gesture recognition using CNNs on two streams, respectively. Finally, a fusion layer is designed for fusing the recognition results of the two streams. This method utilizes multimodal data to increase the recognition accuracy of the ASL hand gestures. The experiments prove that the recognition accuracy of 2S-CNN can reach 92.08\(\%\) on ASL fingerspelling database and is higher than that of baseline methods.

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References

  1. Goodrich, M.A., Schultz, A.C.: Human crobot interaction: a survey. Found. Trends in Hum. Comput. Interact. 1(3), 203–275 (2008)

    Article  Google Scholar 

  2. Liu, J., Luo, Y., Ju, Z.: An interactive astronaut-robot system with gesture control. Comput. Intell. Neurosci. 2016, 7845102 (2016)

    Google Scholar 

  3. Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43(1), 1–54 (2015)

    Article  Google Scholar 

  4. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)

    Article  Google Scholar 

  5. Wang, T., Li, Y., Hu, J., Khan, A., Liu, L., Li, C., Ran, M.: A survey on vision-based hand gesture recognition. In: International Conference on Smart Multimedia, pp. 219-231. August 2018

    Google Scholar 

  6. Oyedotun, O.K., Khashman, A.: Deep learning in vision-based static hand gesture recognition. Neural Comput. Appl. 28(12), 3941–3951 (2017)

    Article  Google Scholar 

  7. Nagi, J., Ducatelle, F., Di Caro, A.G., Cirean, D., Meier, U., Giusti, A., Gambardella, L.M.: Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: Conference on 2011 IEEE International In Signal and Image Processing Applications (ICSIPA), pp. 342-347 (2011)

    Google Scholar 

  8. Kimm, Y., Toomajian, B.: Hand gesture recognition using micro-Doppler signatures with convolutional neural network. IEEE Access 4, 7125–7130 (2016)

    Article  Google Scholar 

  9. Yamashita, T., Watasue, T.: Hand posture recognition based on bottom-up structured deep convolutional neural network with curriculum learning. In: Image Processing (ICIP), 2014 IEEE International Conference on, pp. 853-857 (2014)

    Google Scholar 

  10. Gao, Q., Liu, J., Ju, Z., Li, Y., Zhang, T., Zhang, L.: Static hand gesture recognition with parallel CNNs for space human-robot interaction. In: International Conference on Intelligent Robotics and Applications, pp. 462-473 (2017)

    Chapter  Google Scholar 

  11. Flores, C.J.L., Cutipa, A.G., Enciso, R.L.: Application of convolutional neural networks for static hand gestures recognition under different invariant features. In: 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pp. 1-4 (2017)

    Google Scholar 

  12. Zhang, Z., Tian, Z., Zhou, M.: HandSense: smart multimodal hand gesture recognition based on deep neural networks. Journal of Ambient Intelligence and Humanized Computing, 1-16 (2018)

    Google Scholar 

  13. Hao, S., Wang, W., Ye, Y., Nie, T., Bruzzone, L.: Two-stream deep architecture for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(4), 2349–2361 (2018)

    Article  Google Scholar 

  14. Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimedia 19(2), 4–10 (2012)

    Article  Google Scholar 

  15. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. AAAI 4, 12 (2017)

    Google Scholar 

  16. Simonyan, K., Zisserman, A. : Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  17. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision, pp. 630-645 (2016)

    Chapter  Google Scholar 

  18. Pugeault, N., Bowden, R.: Spelling it out: real-time ASL fingerspelling recognition. In: 2011 IEEE International Conference on, Computer Vision Workshops (ICCV Workshops), pp. 1114-1119 (2011)

    Google Scholar 

  19. Kuznetsova, A., Leal-Taix, L. , Rosenhahn, B.: Real-time sign language recognition using a consumer depth camera. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 83-90 (2013)

    Google Scholar 

  20. Keskin, C., Kraç, F., Kara, Y.E., Akarun, L.: Real time hand pose estimation using depth sensors. In Consumer depth cameras for computer vision, 119-137 (2013)

    Chapter  Google Scholar 

  21. Dong, C., Leu, M.C., Yin, Z.: American sign language alphabet recognition using microsoft Kinect. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 44-52 (2015)

    Google Scholar 

Download references

Acknowledgment

Research supported in part by the Key Research Program of the Chinese Academy of Sciences under Grant Y4A3210301, in part by the Research Fund of China Manned Space Engineering under Grant 050102, in part by the Natural Science Foundation of China under Grant 51775541, 51575412, 51575338 and 51575407, in part by the EU Seventh Framework Programme (FP7)-ICT under Grant 611391, in part by the Research Project of State Key Lab of Digital Manufacturing Equipment & Technology of China under Grant DMETKF2017003, in part by National Key R&D Program Projects 2018YFB1304600.

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Authors and Affiliations

  1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Qing Gao, Jinguo Liu & Zhaojie Ju

  2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China

    Qing Gao, Jinguo Liu & Zhaojie Ju

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Qing Gao

  4. School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, UK

    Uchenna Emeoha Ogenyi, Zhaojie Ju & Honghai Liu

Authors
  1. Qing Gao
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  2. Uchenna Emeoha Ogenyi
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  3. Jinguo Liu
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  4. Zhaojie Ju
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  5. Honghai Liu
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Corresponding author

Correspondence to Jinguo Liu .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, UK

    Zhaojie Ju

  2. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK

    Longzhi Yang

  3. Bristol Robotics Laboratory, University of the West of England, Bristol, UK

    Chenguang Yang

  4. School of Computing, University of Portsmouth, Portsmouth, Hampshire, UK

    Alexander Gegov

  5. School of Computing, University of Portsmouth, Portsmouth, UK

    Dalin Zhou

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Gao, Q., Ogenyi, U.E., Liu, J., Ju, Z., Liu, H. (2020). A Two-Stream CNN Framework for American Sign Language Recognition Based on Multimodal Data Fusion. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_9

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