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References

    1. 1)
      • 1. Pisharady, P.K., Saerbeck, M.: ‘Recent methods and databases in vision-based hand gesture recognition: a review[J]’, Comput. Vis. Image Underst., 2015, 141, pp. 152165.
    2. 2)
      • 2. Hasan, H., Abdul-Kareem, S.: ‘Human–computer interaction using vision-based hand gesture recognition systems: a survey[J]’, Neural Comput. Appl., 2014, 25, (2), pp. 251261.
    3. 3)
      • 3. Khan, R.Z., Ibraheem, N.A.: ‘Hand gesture recognition: a literature review[J]’, Int. J. Artif. Intell. Appl., 2012, 3, (4), p. 161.
    4. 4)
      • 4. Zhang, Z.: ‘Microsoft kinect sensor and its effect[J]’, IEEE Multimed., 2012, 19, (2), pp. 410.
    5. 5)
      • 5. Szegedy, C., Liu, W., Jia, Y., et al: ‘Going deeper with convolutions[C]’. IEEE Comput. Vis. Pattern Recognit., 2015, pp. 19, 1, (9).
    6. 6)
      • 6. Simonyan, K., Zisserman, A.: ‘Very deep convolutional networks for large-scale image recognition[J]’, arXiv preprint arXiv:1409.1556, 2014.
    7. 7)
      • 7. Szegedy, C., Vanhoucke, V., Ioffe, S., et al: ‘Rethinking the inception architecture for computer vision[C]’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, USA, June 2016, pp. 28182826.
    8. 8)
      • 8. Gulshan, V., Peng, L., Coram, M., et al: ‘Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]’, JAMA, 2016, 316, (22), pp. 24022410.
    9. 9)
      • 9. Esteva, A., Kuprel, B., Novoa, R.A., et al: ‘Dermatologist-level classification of skin cancer with deep neural networks[J]’, Nature, 2017, 542, (7639), p. 115.
    10. 10)
      • 10. Pugeault, N., Bowden, R.: ‘Spelling it out: real-time ASL fingerspelling recognition[C]’. IEEE Int. Conf. on Computer Vision Workshops, Barcelona, Spain, November 2011, pp. 11141119.
    11. 11)
      • 11. Estrela, B., Camarachavez, G., Campos, M.F., et al: ‘Sign language recognition using partial least squares and RGB-D information[C]’. Proceedings of the IX Workshop de Visao Computacional, WVC. 2013, Rio de Janeiro, Brazil, June 2013.
    12. 12)
      • 12. Zhang, C., Yang, X., Tian, Y.L.: ‘Histogram of 3D facets: a characteristic descriptor for hand gesture recognition[C]’. IEEE Int. Conf. and Workshops on Automatic Face and Gesture Recognition, Shanghai, China, April 2013, pp. 18.
    13. 13)
      • 13. Han, M., Chen, J., Li, L., et al: ‘Visual hand gesture recognition with convolution neural network[C]’. IEEE/acis Int. Conf. on Software Engineering, Artificial Intelligence, NETWORKING and Parallel/distributed Computing, Shanghai, China, June 2016, pp. 287291.
    14. 14)
      • 14. Shih, H.C., Liu, E.R.: ‘Machine-to-machine interaction based on remote 3D arm pointing using single RGBD camera[J]’, Lect. Notes Electr. Eng., 2014, 260, pp. 11091114.
    15. 15)
      • 15. Chen, X., Koskela, M.: ‘Using appearance-based hand features for dynamic RGB-D gesture recognition[C]’. IEEE Int. Conf. on Pattern Recognition, Stockholm, Sweden, August 2014, pp. 411416.
    16. 16)
      • 16. Li, Y., Wang, X., Liu, W., et al: ‘Deep attention network for joint hand gesture localization and recognition using static RGB-D images[J]’, Inf. Sci., 2018, 441, pp. 6678.
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