Abstract
Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the fingerprint recognition technique to check criminal background of person. Our system working with two parallel process that are fingerprint recognition and matching with personal profile in database. We also demonstrated that an evaluated the experimental with two types that are match and not match to any profile in database which can used in automatic recognition systems. The efficiency of our system is excellent.
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
Fujun, F., Xinshe, L., Litao, W.: Design and implementation of identity authentication system based on fingerprint recognition and cryptography. In: 2016 2nd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China, pp. 254–257 (2016). https://doi.org/10.1109/compcomm.2016.7924704
Soewito, B., Gaol, F.L., Simanjuntak, E., Gunawan, F.E.: Smart mobile attendance system using voice recognition and fingerprint on smartphone. In: 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), Lombok, pp. 175–180 (2016). https://doi.org/10.1109/isitia.2016.7828654
Ali, M.M.H., Mahale, V.H., Yannawar, P., Gaikwad, A.T.: Overview of fingerprint recognition system. In: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, pp. 1334–1338 (2016)
Hnoohom, N., Chumuang, N., Ketcham, M.: Thai handwritten verification system on documents for the investigation. In: 2015 11th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), Bangkok, pp. 617–622 (2015)
Chumuang, N., Ketcham, M.: Intelligent handwriting Thai Signature Recognition System based on artificial neuron network. In: TENCON 2014 – 2014 IEEE Region 10 Conference, Bangkok, pp. 1–6 (2014). https://doi.org/10.1109/tencon.2014.7022415
Lin, C., Kumar, A.: Improving cross sensor interoperability for fingerprint identification. In: 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, pp. 943–948 (2016). https://doi.org/10.1109/icpr.2016.7899757
Patil, A.P., Bhalke, D.G.: Fusion of fingerprint, palmprint and iris for person identification. In: 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), Pune, pp. 960–963 (2016). https://doi.org/10.1109/icacdot.2016.7877730
Yani, W., Zhendong, W., Jianwu, Z., Hongli, C.: A robust damaged fingerprint identification algorithm based on deep learning. In: 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi’an, pp. 1048–1052 (2016)
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
Chumuang, N., Ketcham, M., Sawatnatee, A. (2019). Criminal Background Check Program with Fingerprint. In: Theeramunkong, T., et al. Advances in Intelligent Informatics, Smart Technology and Natural Language Processing. iSAI-NLP 2017. Advances in Intelligent Systems and Computing, vol 807. Springer, Cham. https://doi.org/10.1007/978-3-319-94703-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-94703-7_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94702-0
Online ISBN: 978-3-319-94703-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)