Evaluation of a Fingerprint Recognition Technology for a Biometric Security System
American Journal of Computer Science and Technology
Volume 1, Issue 4, December 2018, Pages: 74-84
Received: Nov. 5, 2018;
Accepted: Nov. 27, 2018;
Published: Dec. 26, 2018
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Chinedu Paschal Uchenna, Department of Information Technology, National Open University of Nigeria (NOUN), Abuja, Nigeria
Adegher Pascal, Department of Information Technology, National Open University of Nigeria (NOUN), Abuja, Nigeria
Ogundu Prince, Department of Maths and Computer Science, National Open University of Nigeria (NOUN), Abuja, Nigeria
Authentication is a fundamental component of human interaction with computers. Traditional means of authentication, primarily password and personal identification numbers (PINs), have until recently dominated computing, and are likely to remain essential for the years to come. Over the years, passwords are kept simple to avoid them being easily forgotten. This has subjected them to higher vulnerability to much compromise by unauthorized persons. Thus, computers are forced to manage more and more passwords which imply that the likelihood of password being forgotten increases. Hence, biometrics is becoming more convenient and distinctly more precise than traditional methods such as passwords and PINs. Biometrics link the event to a particular individual, requires nothing to remember or carry, provides positive confirmation by verifying individuals are who they claim to be, and is becoming an inexpensive solution. This research work aims to evaluate fingerprint recognition technology for a biometric security system using the implementation at the Council for the Regulation of Engineering in Nigeria (COREN) as a case study; to reveal the enormous benefits of using or deploying biometrics which may include the increased security, increased convenience, reduce fraud, or delivery of enhanced services. An evaluation of the said system as deployed in COREN was advanced by the use of quantitative method which design administered a survey instrument in the form of a questionnaire among 20 employees of Council for Regulation of Engineering in Nigeria (COREN). Therefore, upon analyzing the responses from the field work conducted, it is evident that fingerprint recognition system is secured due to fingerprints uniqueness. The outcome of the system suggests that fingerprints, sensors and papillary line provide enough entropy on biometric security.
Chinedu Paschal Uchenna,
Evaluation of a Fingerprint Recognition Technology for a Biometric Security System, American Journal of Computer Science and Technology.
Vol. 1, No. 4,
2018, pp. 74-84.
Jain, A., Dass, S. C. & Nandakumar, K. (2004). “Soft biometric traits for personal recognition system,” in Proc. ICBA, 2004, vol. 3072, LNCS, pp. 731–738.
Opara, E. U., Rob, M. & Etnyre, V. (2016). Biometric and Systems Security: An Overview of End-To-End Security System. Communications of the IIMA Vol. 6, no. 2, pp. 53-57. Retrieved November 23, 2018 from https://www.researchgate.net/publication/265260378_Biometric_and_Systems_Security_An_Overview_of_End-To-End_Security_System
Kumar, A., Berg, A. C., Belhumeur, P. N. & Nayar, S. K. (2009). “Attribute and simile classifiers for face verification,” in IEEE Int. Conf. Computer Vision (ICCV), 2009, pp. 1–8.
Pierrard, B. S. & Vetter, T. (2007). “Skin detail analysis for face recognition,” in Proc. CVPR, 2007, pp. 1–8.
Faundez-Zanuy, M. (2016). Biometric security technology. IEEE Aerospace and Electronic Systems Magazine, Vol. 21 no 6, pp. 15-26, ISSN: 0885-8985. June 2006.
Bontrager, P., Roy, A., Togelius, J., Memon, N, & Ross, A. (2018). DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution. Retrieved November 23, 2018 from https://arxiv.org/pdf/1705.07386.pdf
Roy, A., Memon, N., Togelius, J., & Ross, A. (2018). Evolutionary methods for generating synthetic masterprint templates: Dictionary attack in fingerprint recognition. In International Conference on Biometrics, pages 1–8, 2018.
Bose, P. K. & Kabir, M. J. (2017). Fingerprint: A Unique and Reliable Method for Identification. Journal of Enam Medical College Vol 7 No 1 January 2017. Retrieved November 23, 2018 from doi: http://dx.doi.org/10.3329/jemc.v7i1.30748
Thompson, W. C., Vuille, J., Taroni, F. & Biedermann, A. (2018). After Uniqueness: The Evolution of Forensic-Science Opinions. Judicature. Vol. 102 No 1 Spring 2018. Retrieved November 23, 2018 from https://judicialstudies.duke.edu/wp-content/uploads/2018/04/JUDICATURE102.1-THOMPSON-etal-1.pdf
Hu, Y., Li, M., Ma, W. & Zhang, H. (2004). “Efficient propagation for face annotation in family albums,” in Proc. ACM Int. Conf. Multimedia, 2004, pp. 716–723.
Beslay, L. Galbally, J. & Haraksim, R. (2018). Automatic fingerprint recognition: from children to elderly. Ageing and age effects. JRC Technical Report, the European Commission’s science and knowledge service. ISBN 978-92-79-87179-5 ISSN 1831-9424. Retrieved November 23, 2018 from doi: 10.2760/809183.
Muchtar, M. A., Seniman, D Arisandi, D. & Hasanah, S. (2018). Attendance fingerprint identification system using arduino and single board computer. IOP Conf. Series: Journal of Physics: Conf. Series 978 (2018) 012060 doi : 10.1088/1742-6596/978/1/012060.
Choi, J. Y., Yang, S., Ro, Y. M. & Plataniotis, K. N. (2008). “Face annotation for personal photos using context-assisted face recognition,” in Proc.ACM Int. Conf. Multimedia Information Retrieval, 2008, pp. 44–51.
Jaewon- Sung, T. Takeo, K., & Daijin, K. (2007). “A Unified Gradient-Based Approach for Combining ASM into AAM” International Journal of Computer Vision 75 (2), 297–309, 2007.
ŐSZI, A. & RUIZ, L. S. (2016). Biometric Uses in Occupational Safety and Health. Vol. 11 no 4, December 2016. Retrieved November 23, 2018 from http://hadmernok.hu/164_01_arnold.pdf