Abstract
Due to biometric immutability, an authentication system that depends on irrevocable biometric data (faces and fingerprints) is vulnerable to vicious attacks. Gestures, as short actions that contain static and dynamic behavioral information, are gradually replacing traditional biometrics. Compared to body gestures, hand gestures are more flexible and do not require the user’s entire body to appear in front of the camera. However, most existing feature extraction algorithms rely on the key point of a hand in motion or the image analysis of a static hand gesture, thereby making the authentication less real-time and less effective in the real-word. To alleviate these problems, we propose a user authentication system based on dynamic hand gestures jointly models the silhouette and skeletal properties of moving hands for user authentication. Our system obtains an average 0.105% false acceptance rate (FAR) and an average 3.40% false rejection rate (FRR) on the public Dynamic Hand Gesture 14/28 dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Narayana, P., Beveridge, J.R., Draper, B.A., et al.: Gesture recognition: focus on the hands. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5235–5244 (2018)
Zhang, P., Lan, C., Xing, J., et al.: View adaptive recurrent neural networks for high performance human action recognition from skeleton data. In: IEEE International Conference on Computer Vision, pp. 2136–2145, August 2017
Chen, X., Guo, H., Wang, G., Zhang, L.: Motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition. In: IEEE International Conference on Image Processing, pp. 2881–2885, August 2017
Wu, Y., Ji, W., Li, X.: Context-aware deep spatio-temporal network for hand pose estimation from depth images. IEEE Trans. Cybern. arXiv:1810.02994v1 [cs.CV], 6 October 2018
Yuan, S., Stenger, B., Kim, T.-K.: RGB-based 3D hand pose estimation via privileged learning with depth images. arXiv:1811.07376v1 [cs.CV], 18 November 2018
Hu, T., Wang, W., Lu, T.: Hand pose estimation with attention-and-sequence network. In: Pacific Rim Conference on Multimedia PCM 2018. Advances in Multimedia Information Processing, pp. 556–566 (2018)
Lai, K., Konrad, J., Ishwar, P.: Towards gesture-based user authentication. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (2012)
Fong, S., Zhuang, Y., Fister, I., Fister Jr., I.: A biometric authentication model using hand gesture images. Biomed. Eng. Online 12, 111 (2013)
Wu, J., Konrad, J., Ishwar, P.: Dynamic time warping for gesture-based user identification and authentication with kinect. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2371–2375 (2013)
Aumi, Md.T.I., Kratz, S.: AirAuth: evaluating in-air hand gestures for authentication. In: MobileHCI 2014, Toronto, ON, CA, 23–26 September 2014
Kviatkovsky, I., Shimshoni, I., Rivlin, E.: Person Identification from action styles. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (2015)
Wu, J., Christianson, J., Konrad, J., Ishwar, P.: Leveraging shape and depth in user authentication from in-air hand gestures. In: 2015 IEEE International Conference on Image Processing, pp. 3195–3199 (2015)
Sharma, A., Sundaram, S.: An enhanced contextual DTW based system for online signature verification using vector quantization. Pattern Recognit. Lett. 84, 22–28 (2016)
Guo, K., Ishwar, P., Konrad, J.: Action recognition from video using feature covariance matrices. IEEE Trans. Image Process. 22(6), 2479–2494 (2013)
Wang, W., Zhang, J., Wu, W.: An automatic approach for retinal vessel segmentation by multi-scale morphology and seed point tracking. J. Med. Imaging Health Inform. 8, 262–274(13) (2018)
Lee, M., Lee, S., Choi, M.-J., et al.: Hybrid FTW: hybrid computation of dynamic time warping distances. IEEE Access 6, 2085–2096 (2017)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 61573151, in part by Science and Technology Planning Project of Guangdong Province under Grant 2018B030323026, and in part by the Fundamental Research Funds for the Central Universities under Grant 2018PY24.
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
Liu, C., Kang, W., Fang, L., Liang, N. (2019). Authentication System Design Based on Dynamic Hand Gesture. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-31456-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31455-2
Online ISBN: 978-3-030-31456-9
eBook Packages: Computer ScienceComputer Science (R0)