Abstract
A vision-based static hand gesture recognition algorithm which consists of three stages: pre-processing, feature extraction and classification are presented in this work. The pre-processing stage comprises of following three sub-stages: segmentation, which segments hand region from its background using YCbCr skin colour-based segmentation process; rotation, that rotates segmented gesture to make the algorithm, rotation invariant; Morphological filtering, that removes background and object noise. Non-orthogonal moments like geometric moments and orthogonal moments like Tchebichef and Krawtchouk moments are used here as features. To improve the performance of classification, two feature fusion strategies are proposed in this work: serial feature fusion and parallel feature fusion. A feed-forward multi-layer perceptron (MLP)-based artificial neural network classifier is proposed. A user-independent experiment is conducted on 1,500 gestures of 10 classes for 10 different users.
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References
Gupta, S., Jaafar, J., Ahmad, W.F.W.: Static hand gesture recognition using local gabor filter. In: International Symposium on Robotics and Intelligent Sensors (IRIS 2012), vol. 41, pp. 827–832, Kuching, Sarawak, Malaysia (2012)
Priyal, S.P., Bora, P.K.: A robust static hand gesture recognition system using geometry based normalization and krawtchouk moments. Pattern Recogn. 46(8), 2202–2219 (2013)
Kumar, P., Verma, J., Prasad, S.: Hand data glove: A wearable real-time device for human-computer interaction. Int. J. Adv. sci. Technol. 43 (2012)
Huang, Y., Monekosso, D., Wang, H., Augusto, J.C.: A concept grounding approach for glove-based gesture recognition. In: 7th International conference On Intelligent Environments (IE), pp. 358–361, Nottingham, UK (2011)
Yang, J., Yang, J.Y., Jhang, D., Lu, J.F.: Feature fusion: Parallel strategy versus serial strategy. Pattern Recogn. 36(6), 1369–1381 (2003)
Aibinu, A.M., Shafie, A.A., Salami, M.J.E.: Performance analysis of ANN based YCbCr Skin Color Detection Algorithm. In: International Symposium on Robotics and Intelligent Sensors (IRIS), vol. 41, pp. 1183–1189, Sarawak, Malaysia (2012)
Ghosh, D.K., Ari, S.: A static hand gesture recognition algorithm using K-Mean based radial basis function neural network. In: 8th International Conference on Information, Communications and Signal Processing (ICICS), pp. 1–5, Singapore (2011)
Priyal, S.P., Bora, P.K.: A study on static hand gesture recognition using moments. In: International Conference on Signal Processing and Communications (SPCOM), pp. 1–5, IISC, Bangalore (2010)
Zou, Z., Premaratne, P., Monaragala, R., Bandara, N Premaratne, M.: Dynamic hand gesture recognition system using moment invariants. In: 5th International Conference on Information Automation for Sustainability (ICIAFs), pp. 108–113, Colombo, Sri Lanka (2010)
Haykin, S.: Neural networks. Prentice-Hall (1999, 2nd edn.)
Daamouche, A., Hamami, L., Alajlan, N., Melgani, F.: A wavelet optimization approach for ECG signal classification. Biomed. Signal Process. Control 7(4), 342–349 (2012)
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Chatterjee, S., Ghosh, D.K., Ari, S. (2015). Static Hand Gesture Recognition Based on Fusion of Moments. In: Jain, L., Patnaik, S., Ichalkaranje, N. (eds) Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 309. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2009-1_48
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DOI: https://doi.org/10.1007/978-81-322-2009-1_48
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