Abstract
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.
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References
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)
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)
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)
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)
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)
Lippmann, G.: Epreuves reversibles donnant la sensation du relief. J. Phys. Theor. Appl. 7(1), 821–825 (1908)
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)
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)
Russell, S.J. Norvig, P.: Artificial Intelligence: A Modern Approach (International Edition) (2002)
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)
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)
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)
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)
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)
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Qin, R. et al. (2020). A Fast Automatic Holoscopic 3D Micro-gesture Recognition System for Immersive Applications. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_74
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DOI: https://doi.org/10.1007/978-3-030-32591-6_74
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