Abstract
Hand posture recognition is an active research topic in computer vision and robotics with many applications ranging from automatic sign language recognition to human-system interaction. Recently, we have proposed a new descriptor for hand representation based on the kernel method (KDES) [1]. Our new descriptor inherits the main idea of KDES but we proposed three improvements to make it more robust. One of the improvements was that we introduced a new hand pyramid structure [14]. Intuitively, hand pyramid is more suitable to hand structure than conventional pyramid. In our previous work, we have demonstrated that the combination of improvements to KDES gives more accurate hand posture classification than using original KDES. However, it still lacks discussions and experimental evidences of the contribution of hand pyramid for hand representation. In this paper, we build specific hand dataset and conduct more experiments to show how hand pyramid contributes for hand representation. We will discuss deeply on the results and analyze the impact of this pyramid on hand posture classification.
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References
Bo, L., et al.: Kernel descriptors for visual recognition. In: NIPS, pp. 1–9 (2010)
Bo, L., Sminchisescu, C.: Efficient match kernel between sets of features for visual recognition. In: NIPS, pp. 1–9 (2009)
Boughnim, N., Marot, J.: Hand posture recognition using jointly optical flow and dimensionality reduction. EURASIP J. Adv. Sig. Process. 2013, 1–22 (2013)
Chaudhary, A., et al.: Intelligent approaches to interact with machines using hand gesture recognition in natural way: a survey. IJCSES 2(1), 122–133 (2011)
Choi, J., Seo, B.: Robust hand detection for augmented reality interface. In: Proceedings of the 8th International Conference on Virtual Reality Continuum and its Applications in Industry, pp. 319–322 (2009)
Dardas, N.H., Georganas, N.D.: Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Trans. Instrum. Meas. 60(11), 3592–3607 (2011)
Francke, H., Ruiz-del-Solar, J., Verschae, R.: Real-time hand gesture detection and recognition using boosted classifiers and active learning. In: Mery, D., Rueda, L. (eds.) PSIVT 2007. LNCS, vol. 4872, pp. 533–547. Springer, Heidelberg (2007). doi:10.1007/978-3-540-77129-6_47
Freeman, W.T., Roth, M.: Orientation histograms for hand gesture recognition. In: International Workshop on Automatic Face and Gesture Recognition, pp. 296–301 (1995)
Gupta, S., et al.: Static hand gesture recognition using local gabor filter. Procedia Eng. 41, 827–832 (2012). Iris
Hasan, M.M., Mishra, P.K.: Hand gesture modeling and recognition using geometric features : a review. CJIPCV 3, 1 (2012)
Kaufmann, B., Louchet, J., Lutton, E.: Hand posture recognition using real-time artificial evolution. In: Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplications 2010. LNCS, vol. 6024, pp. 251–260. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12239-2_26
Lockton, R., Fitzgibbon, A.W.: Real-time gesture recognition using deterministic boosting. In: BMVC, pp. 817–826 (2002)
Marcel, S., Bernier, O.: Hand posture recognition in a body-face centered space. In: Braffort, A., Gherbi, R., Gibet, S., Teil, D., Richardson, J. (eds.) GW 1999. LNCS (LNAI), vol. 1739, pp. 97–100. Springer, Heidelberg (1999). doi:10.1007/3-540-46616-9_9
Nguyen, V.-T., et al.: A new hand representation based on kernels for hand posture recognition. In: The 11th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2015) (2015)
Nguyen, V.-T.: Visual interpretation of hand postures for human-machine interaction (2015)
Ong, E.-J., Bowden, R.: A boosted classifier tree for hand shape detection. In: Sixth IEEE International Conference on Face and Gesture Recognition, pp. 889–894 (2004)
Pavlovic, V.I., et al.: Visual interpretation of hand gestures for human-computer interaction: a review. TPAMI 19(7), 677–695 (1997)
Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. AIR 43, 1–54 (2012)
Sgouropoulos, K., et al.: A dynamic gesture and posture recognition system. J. Intell. Robot. Syst. 76(2), 283–296 (2013)
Stergiopoulou, E., Papamarkos, N.: Hand gesture recognition using a neural network shape fitting technique. EAAI 22(8), 1141–1158 (2009)
Tran, T-T-H Nguyen, T-T-M : Invariant lighting hand posture classification. In: IEEE International Conference on Progress in Informatics and Computing (PIC), pp. 827–831 (2010)
Wachs, J., et al.: A real-time hand gesture system based on evolutionary search. In: GECCO2005: Tenth Genetic and Evolutionary Computation Conference, Washington, DC, USA (2005)
Wu, Y., Huang, T.S.: View-independent recognition of hand postures. In: Computer Vision and Pattern Recognition, pp. 88–94 (2000)
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This research is funded by Hanoi University of Science Technology under grant number T2016-PC-189.
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Nguyen, VT., Le, TL., Tran, TH. (2017). An Evaluation of Hand Pyramid Structure for Hand Representation Based on Kernels. In: Akagi, M., Nguyen, TT., Vu, DT., Phung, TN., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2016. Advances in Intelligent Systems and Computing, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-49073-1_15
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