Abstract
Reliable hand gesture recognition is an important problem for automatic sign language recognition for the people with hearing and speech disabilities. In this paper, we create a new benchmark database of multi-oriented, isolated ASL numeric images using recently launched Kinect V2. Further, we design an effective statistical-topological feature combinations for recognition of the hand gestures using the available V1 sensor dataset and also over the new V2 dataset. For V1, our best accuracy is 98.4% which is comparable with the best one reported so far and for V2 we achieve an accuracy of 92.2% which is first of its kind.
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References
Bai, X., Latecki, L.J.: Path similarity skeleton graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1282–1292 (2008)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)
Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006). doi:10.1007/11744047_33
Debled-Rennesson, I., Feschet, F., Rouyer-Degli, J.: Optimal blurred segments decomposition of noisy shapes in linear time. Comput. Graph. 30(1), 30–36 (2006)
Dewaele, G., Devernay, F., Horaud, R.: Hand motion from 3D point trajectories and a smooth surface model. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 495–507. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24670-1_38
Kerautret, B., Lachaud, J.O.: Meaningful scales detection: an unsupervised noise detection algorithm for digital contours. Image Process. On Line 4, 98–115 (2014)
Kerautret, B., Lachaud, J.O., Said, M.: Meaningful thickness detection on polygonal curve. In: Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, pp. 372–379. SciTePress (2012)
Kry, P.G., Pai, D.K.: Interaction capture and synthesis. ACM Trans. Graph. (TOG) 25, 872–880 (2006). ACM
Mitra, S., Acharya, T.: Gesture recognition: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37(3), 311–324 (2007)
Ngo, P., Nasser, H., Debled-Rennesson, I.: Efficient dominant point detection based on discrete curve structure. In: Barneva, R.P., Bhattacharya, B.B., Brimkov, V.E. (eds.) IWCIA 2015. LNCS, vol. 9448, pp. 143–156. Springer, Cham (2015). doi:10.1007/978-3-319-26145-4_11
Ngo, P., Nasser, H., Debled-Rennesson, I., Kerautret, B.: Adaptive tangential cover for noisy digital contours. In: Normand, N., Guédon, J., Autrusseau, F. (eds.) DGCI 2016. LNCS, vol. 9647, pp. 439–451. Springer, Cham (2016). doi:10.1007/978-3-319-32360-2_34
Nguyen, T.P., Debled-Rennesson, I.: A discrete geometry approach for dominant point detection. Pattern Recogn. 44(1), 32–44 (2011)
Ren, Z., Yuan, J., Meng, J., Zhang, Z.: Robust part-based hand gesture recognition using kinect sensor. IEEE Trans. Multimedia 15(5), 1110–1120 (2013)
Reveillès, J.P.: Gèométrie discrète, calculs en nombre entiersgorithmique, et al.: thèse d’état. Université Louis Pasteur, Strasbourg (1991)
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)
Paul, S., Basu, S.: Microsoft kinect in gesture recognition: a short review. Int. J. Control Theor. Appl. 8(5), 2071–2076 (2015)
Suarez, J., Murphy, R.R.: Hand gesture recognition with depth images: a review. In: RO-MAN, 2012 IEEE, pp. 411–417. IEEE (2012)
Wang, C., Liu, Z., Chan, S.C.: Superpixel-based hand gesture recognition with kinect depth camera. IEEE Trans. Multimedia 17(1), 29–39 (2015)
WEKA: Fibonacci notes (1996). http://www.cs.waikato.ac.nz/ml/weka/downloading.html
Wu, Y., Lin, J., Huang, T.S.: Analyzing and capturing articulated hand motion in image sequences. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1910–1922 (2005)
Yang, M.H., Ahuja, N., Tabb, M.: Extraction of 2D motion trajectories and its application to hand gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1061–1074 (2002)
Zhang, C., Yang, X., Tian, Y.: Histogram of 3D facets: a characteristic descriptor for hand gesture recognition. In: 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–8. IEEE (2013)
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Paul, S., Nasser, H., Nasipuri, M., Ngo, P., Basu, S., Debled-Rennesson, I. (2017). A Statistical-Topological Feature Combination for Recognition of Isolated Hand Gestures from Kinect Based Depth Images. In: Brimkov, V., Barneva, R. (eds) Combinatorial Image Analysis. IWCIA 2017. Lecture Notes in Computer Science(), vol 10256. Springer, Cham. https://doi.org/10.1007/978-3-319-59108-7_20
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DOI: https://doi.org/10.1007/978-3-319-59108-7_20
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