Abstract
Gesture recognition is one of the key technologies in the field of computer vision, and hand gesture recognition can be divided into static hand gesture recognition and the dynamic hand gesture recognition. This paper presents a new static gesture recognition algorithm based on hidden markov model. It uses two kinds of new shape features, the specific angle shape entropy feature and the upper side contour feature. They are firstly used for parameters training of hidden makov model, and then identify gesture categories hierarchically. In order to further improve the recognition effect for those small shape differences gesture, this paper adopts wavelet texture energy feature which can reflect the internal details of the gesture image, and makes the final correction estimation based on minimum total error probability. The experimental results show that the method has good recognition effects for gestures no matter the shape differences are big or not, and it has good real time performance as well.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43, 1–54 (2015)
Pisharady, P.K., Vadakkepat, P., Loh, A.P.: Attention based detection and recognition of hand postures against complex backgrounds. Int. J. Comput. Vis. 10, 403–419 (2013)
Ren, Z., Yuan, J., Zhang, Z.: Robust hand gesture recognition based on finger-earth mover’s distance with commodity depth camera. IEEE Trans. Multimed. 15(5), 1110–1120 (2013)
Singh, M., Mandal, M., Basu, A.: Visual gesture recognition for ground air traffic control using the radon transform. In: International Conference on Intelligent Robots and Systems, pp. 2586–2591 (2005)
Jiang, L.: Research of Gesture Recognition Based on CAS-Glove. Jiaotong University, Beijing (2006)
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)
Xu, X.: Hand Gesture Recognition based on Hidden Markov Module. University of Technology, Guangzhou (2011)
Wainwright, M.J., Simoncelli, E.P., Willsky, A.S.: Random cascades on wavelet trees and their use in analyzing and modeling natural images. Appl. Comput. Harmonic Anal. 11(1), 89–123 (2001)
Wu, X., Wang, K., Zhang, D.: Wavelet energy features extraction and matching for palmprint recognition. J. Comput. Sci. Technol. 20(5), 411–418 (2005)
Wu, X., Wang, K., Zhang, D.: Wavelet based palmprint recognition. In: IEEE Proceedings of the International Conference on Machine Learning Cybernetics, USA, pp. 1253–1257 (2002)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 257–286 (1989)
Wu, X., Zhang, D., Wang, D.: Palmprint recognition. Science Press (2006)
Acknowledgement
This work is supported by the Harbin Science and Technology Bureau outstanding subject leader fund project (2017RAXXJ055), Nature Science Foundation of Heilongjiang Province (F2018020).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhang, L., Zhang, Y., Niu, L., Zhao, Z., Han, X. (2019). HMM Static Hand Gesture Recognition Based on Combination of Shape Features and Wavelet Texture Features. In: Jia, M., Guo, Q., Meng, W. (eds) Wireless and Satellite Systems. WiSATS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-030-19156-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-19156-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19155-9
Online ISBN: 978-3-030-19156-6
eBook Packages: Computer ScienceComputer Science (R0)