Abstract
Depth cameras support working in a dark environment, and provide depth information from objects to cameras, hence have advantages over color cameras. So in this paper we adopt depth cameras to collect accurate gesture information for 3D modeling, in order to obtain accurate gesture recognition. On the depth map, we present methods of hand joint segmentation with random forest pixel classification and of gesture recognition with template matching, which provides accurate judgment for static gestures. Rotation may occur while the acquisition of hand data, so we conduct rotation correction by using SVD decomposition. Experimental results illustrate that this method provides more accurate joint segmentation, which is robust to hand rotation and achieves a recognition rate of 94.8% on ASL dataset.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Feng, Z., Yang, B., Chen, Y., et al.: Features extraction from hand images based on new detection operators. Pattern Recognit. 44(5), 1089–1105 (2011)
Guo, S., Zhang, M., Pan, Z., et al.: Gesture recognition based on pixel classification and contour extraction. In: International Conference on Virtual Reality and Visualization, pp. 93–100. IEEE (2015)
Ye, Q., Yuan, S., Kim, T.-K.: Spatial attention deep net with partial PSO for hierarchical hybrid hand pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 346–361. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_21
Klema, V., Laub, A.J.: The singular value decomposition: its computation and some applications. IEEE Trans. Autom. Control 25(2), 164–176 (1980)
Kuch, J.J., Huang, T.S.: Vision based hand modeling and tracking for virtual teleconferencing and telecollaboration. In: International Conference on Computer Vision, p. 666. IEEE Computer Society (1995)
Liang, H., Yuan, J., Thalmann, D.: Parsing the hand in depth images. IEEE Trans. Multimed. 16(5), 1241–1253 (2014)
Dhruva, N., Rupanagudi, S.R., Sachin, S.K., et al.: Novel segmentation algorithm for hand gesture recognition. In: International Multi-Conference on Automation Computing Communication Control and Compressed Sensing, pp. 383–388. IEEE (2013)
Hachaj, T., Ogiela, M.R., Piekarczyk, M.: Dependence of Kinect sensors number and position on gestures recognition with gesture description language semantic classifier. In: Computer Science and Information Systems, pp. 571–575. IEEE (2013)
Shotton, J., Fitzgibbon, A., Cook, M., et al.: Real-time human pose recognition in parts from single depth images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1297–1304. IEEE Computer Society (2011)
Rafi, U., Gall, J., Leibe, B.: A semantic occlusion model for human pose estimation from a single depth image. In: Computer Vision and Pattern Recognition Workshops, pp. 67–74. IEEE (2015)
Ionescu, C., Carreira, J., Sminchisescu, C.: Iterated second-order label sensitive pooling for 3D human pose estimation. In: Computer Vision and Pattern Recognition, pp. 1661–1668. IEEE (2014)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Yao, Y., Fu, Y.: Real-time hand pose estimation from RGB-D sensor. In: IEEE International Conference on Multimedia and Expo, pp. 705–710. IEEE Computer Society (2012)
Dong, C., Ming, C.L., Yin, Z.: American sign language alphabet recognition using Microsoft Kinect. In: Computer Vision and Pattern Recognition Workshops, pp. 44–52. IEEE (2015)
Acknowledgments
This work was supported by Project of Science and Technology Program of Guangzhou (grant no. S201604016034), Project of Science and Technology Program of Guangdong (grant no. 2017B010110015).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gao, J., Zhan, Y. (2019). Static Gesture Recognition Method Based on 3D Human Hand Joints. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_49
Download citation
DOI: https://doi.org/10.1007/978-3-030-23712-7_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23711-0
Online ISBN: 978-3-030-23712-7
eBook Packages: Computer ScienceComputer Science (R0)