A Fast Automatic Holoscopic 3D Micro-gesture Recognition System for Immersive Applications

  • Rui Qin
  • Yi Liu
  • Mohammad Rafiq Swash
  • Maozhen Li
  • Hongying MengEmail author
  • Tao Lei
  • Tong Chen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


Immersive technology attempts to emulate a physical world through the means of a digital or simulated world. Micro-gestures are small variation actions on human hands defined by user that is one of the most convenient human action in immersive technology. Holoscopic 3D imaging uses bionics technology to capture spatial image in the pattern of “fly’s eye” and it has fruitful 3D cubic information compared to 2D images that can be used for high accurate micro-gesture controller systems. In this paper, a new micro-gesture recognition system based on holoscopic 3D imaging system is proposed for immersive applications. It is built on fast pre-processing, dynamic image feature extraction and a non-linear Support Vector Machine classifier. It is evaluated on the public Holoscopic Micro 3D Gesture (HoMG) dataset outperforming all the existing state-of-the-art methods on the same dataset.


Holoscopic 3D imaging Micro-gesture recognition LPQTOP Support Vector Machine 


  1. 1.
    Aggoun, A., Tsekleves, E., Swash, M.R., Zarpalas, D., Dimou, A., Daras, P., Nunes, P., Soares, L.D.: Immersive 3D holoscopic video system. IEEE MultiMed. 20(1), 28–37 (2013)CrossRefGoogle Scholar
  2. 2.
    Angelini, L., Carrino, F., Carrino, S., Caon, M., Lalanne, D., Khaled, O.A., Mugellini, E.: Opportunistic synergy: a classifier fusion engine for micro-gesture recognition. In: Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 30–37 (2013)Google Scholar
  3. 3.
    Auffarth, B., López, M., Cerquides, J.: Comparison of redundancy and relevance measures for feature selection in tissue classification of CT images. In: Industrial Conference on Data Mining, pp. 248–262. Springer (2010)Google Scholar
  4. 4.
    Cai, Y., Huang, T., Hu, L., Shi, X., Xie, L., Li, Y.: Prediction of lysine ubiquitination with mrmr feature selection and analysis. Amino Acids 42, 1387–95 (2011)CrossRefGoogle Scholar
  5. 5.
    Jiang, B., Valstar, M.F., Pantic, M.: Action unit detection using sparse appearance descriptors in space-time video volumes. In: IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (FG 2011), pp. 314–321. IEEE (2011)Google Scholar
  6. 6.
    Lippmann, G.: Epreuves reversibles donnant la sensation du relief. J. Phys. Theor. Appl. 7(1), 821–825 (1908)CrossRefGoogle Scholar
  7. 7.
    Liu, Y., Meng, H., Swash, M.R., Gaus, Y.F.A., Qin, R.: Holoscopic 3D micro-gesture database for wearable device interaction. In: 2018 13th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018), pp. 802–807. IEEE (2018)Google Scholar
  8. 8.
    Ojansivu, V., Rahtu, E., Heikkila, J.: Rotation invariant local phase quantization for blur insensitive texture analysis. In: 19th International Conference on Pattern Recognition (ICPR 2008), pp. 1–4. IEEE (2008)Google Scholar
  9. 9.
    Russell, S.J. Norvig, P.: Artificial Intelligence: A Modern Approach (International Edition) (2002)Google Scholar
  10. 10.
    Sharma, G., Jyoti, S., Dhall, A.: Hybrid neural networks based approach for holoscopic micro-gesture recognition in images and videos. In: 13th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018), pp. 808–814. IEEE (2018)Google Scholar
  11. 11.
    Tahir, M.A., Chan, C.-H., Kittler, J., Bouridane, A.: Face recognition using multi-scale local phase quantisation and linear regression classifier. In: 18th IEEE International Conference on Image Processing (ICIP), pp. 765–768. IEEE (2011)Google Scholar
  12. 12.
    Wu, C., McCormick, M., Aggoun, A., Kung, S.Y.: Depth mapping of integral images through viewpoint image extraction with a hybrid disparity analysis algorithm. J. Display Technol. 4(1), 101–108 (2008)CrossRefGoogle Scholar
  13. 13.
    Zhang, W., Zhang, W., Shao, J.: Classification of holoscopic 3D micro-gesture images and videos. In: 13th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018), pp. 815–818. IEEE (2018)Google Scholar
  14. 14.
    Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Rui Qin
    • 1
  • Yi Liu
    • 1
  • Mohammad Rafiq Swash
    • 1
  • Maozhen Li
    • 1
  • Hongying Meng
    • 1
    Email author
  • Tao Lei
    • 2
  • Tong Chen
    • 3
  1. 1.Department of Electronic and Computer EngineeringBrunel University LondonLondonUK
  2. 2.Shaanxi University of Science and TechnologyXi’anChina
  3. 3.Southwest UniversityChongqingChina

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