全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

Palmprint Image Acquisition and Analysis System Based on IoT Technology

DOI: 10.4236/oalib.1106897, PP. 1-8

Subject Areas: Technology

Keywords: Image, IoT, Palmprint Texture, Feature Extraction, Multi-Scale, System Design

Full-Text   Cite this paper   Add to My Lib

Abstract

With the rapid development and advancement of society, information technology represented by biometric recognition has developed rapidly. Palmprint images have a larger area, more texture details, rich texture features and distinguishable information, and palmprint recognition is opposite to the palm. The pattern image acquisition equipment and resolution requirements are not high, which is suitable for palmprint image acquisition and analysis under the framework of the Internet of Things (IoT), and then realize the design of recognition technology and system based on palmprint texture characteristics. The palmprint texture feature extraction and analysis algorithm is deployed on the cloud server, and the analysis result is fed back to the on-site collection device to realize the collection and analysis of palmprint images.

Cite this paper

Zhu, Z. , Chen, X. , Tu, Y. and Zhang, X. (2020). Palmprint Image Acquisition and Analysis System Based on IoT Technology. Open Access Library Journal, 7, e6897. doi: http://dx.doi.org/10.4236/oalib.1106897.

References

[1]  Kolivand, H., Fern, B.M., Saba, T., Rahim, M.S.M. and Rehman, A. (2019) A New Leaf Venation Detection Technique for Plant Species Classification. Arabian Journal for Science and Engineering, 44, 3315-3327. https://doi.org/10.1007/s13369-018-3504-8
[2]  Voorhees, H. and Poggio, T. (1987) Detecting Textons and Texture Boundaries in Natural Images. Proceedings of the First International Conference on Computer Vision, London, 8-11 June 1987, 250-258.
[3]  Tomita, F. and Tsuji, S. (1990) Computer Analysis of Visual Textures. Springer International. https://doi.org/10.1007/978-1-4613-1553-7
[4]  Haralick, R.M., Shanmugam, K. and Dinstein, I.H. (1973) Textural Features for Image Classification. IEEE Transactions on SMC, 3, 610-621. https://doi.org/10.1109/TSMC.1973.4309314
[5]  Srinivasa, G.N. and Shobba, G. (2006) Statistical Texture Analysis. Proceedings of World Academy of Science, Engineering and Technology, 11, 196-201.
[6]  Bingöl, Ö. and Ekinci, M. (2017) Stereo-Based Palmprint Recognition in Various 3D Ostures. Expert Systems with Applications, 78, 74-88. https://doi.org/10.1016/j.eswa.2017.01.025
[7]  Tabejamaat, M. and Mousavi, A. (2018) Generalized Gabor Filters for Palmprint Recognition. Pattern Analysis & Applications, 21, 261-275. https://doi.org/10.1007/s10044-017-0638-3
[8]  Pooniaa, P. and Ajmerab, P.K. (2020) Vijayendra Shendec. Palmprint Recognition Using Robust Template Matching. Procedia Computer Science, 167, 727-736. https://doi.org/10.1016/j.procs.2020.03.338
[9]  Zhong, D., Du, X. and Zhong, K. (2019) Decade Progress of Palmprint Recognition: A Brief Survey (Article). Neurocomputing, 328, 16-28. https://doi.org/10.1016/j.neucom.2018.03.081
[10]  Hao, F., Chang, X., Yang, G., Yang, L., Li, C., Li, C. and Xia, C. (2020) Local Image Quality Measurement for Multi-Scale Forensic Palmprints. Multimedia Tools and Applications, 79, 12915-12938. https://doi.org/10.1007/s11042-020-08625-y

Full-Text


Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133