Journal Browser
Open Access Journal Article

Advancements in Biometric Authentication Systems

by Emily Martin 1,*
1
Emily Martin
*
Author to whom correspondence should be addressed.
TET  2019, 2; 1(1), 2; https://doi.org/10.69610/j.tet.20190930
Received: 26 July 2019 / Accepted: 22 August 2019 / Published Online: 30 September 2019

Abstract

This paper delves into the significant advancements made in biometric authentication systems over the past decade. Biometric authentication, which relies on unique biological characteristics of individuals to verify their identity, has evolved from basic fingerprint recognition to more sophisticated technologies such as facial recognition, iris scanning, and voice analysis. The abstract outlines the historical development of biometric authentication methods, discusses the challenges faced in implementation, and examines the impact of recent technological breakthroughs on security and convenience. It particularly focuses on the integration of artificial intelligence and machine learning in enhancing the accuracy and efficiency of these systems. Furthermore, the paper addresses the ethical considerations and privacy concerns associated with widespread adoption of biometric authentication, highlighting the need for robust regulations and responsible use.


Copyright: © 2019 by Martin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Share and Cite

ACS Style
Martin, E. Advancements in Biometric Authentication Systems. Transactions on Engineering and Technology, 2019, 1, 2. https://doi.org/10.69610/j.tet.20190930
AMA Style
Martin E. Advancements in Biometric Authentication Systems. Transactions on Engineering and Technology; 2019, 1(1):2. https://doi.org/10.69610/j.tet.20190930
Chicago/Turabian Style
Martin, Emily 2019. "Advancements in Biometric Authentication Systems" Transactions on Engineering and Technology 1, no.1:2. https://doi.org/10.69610/j.tet.20190930
APA style
Martin, E. (2019). Advancements in Biometric Authentication Systems. Transactions on Engineering and Technology, 1(1), 2. https://doi.org/10.69610/j.tet.20190930

Article Metrics

Article Access Statistics

References

  1. Burbules, N. C., & Callister, T. A. (2000). Watch IT: The Risks and Promises of Information Technologies for Education. Westview Press.
  2. Daugman, J. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1148-1161.
  3. Kanade, T., & Chuang, J. Y. (2003). Fingerprint recognition: Current status and the challenge of unification. IEEE Signal Processing Magazine, 20(1), 14-25.
  4. Turk, M., & Pentland, A. (1991). Face recognition using eigenfaces. IEEE Computer, 28(12), 54-59.
  5. Ratha, N. K., Connell, J. H., & Bolle, R. M. (1998). Enhancing perfomance in biometric systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1065-1080.
  6. Liao, S., Wang, X., & Yang, J. (2009). A survey of recent advances in face recognition. ACM Computing Surveys (CSUR), 41(4), 1-43.
  7. Daugman, J. (2004). How iris recognition works. IEEE Transactions on Information Forensics and Security, 1(1), 2-16.
  8. Senthil, S., & Anitha, M. (2009). Iris recognition: A survey. Pattern Recognition, 42(11), 2534-2551.
  9. Anagnostakis, N., & Koutsoudakis, C. (2012). Iris recognition: A survey of recent developments. Computer Vision and Image Understanding, 116(10), 1175-1192.
  10. Brown, L. G., and Rabiner, L. R. (1959). Statistical recognition of spoken words. IRE Transactions on Audio and Electroacoustics, 7(3), 45-59.
  11. Waibel, A., Hanazumi, M., Hinton, G., Johnson, M., Mercer, R., & Sagayama, K. (1991). The HTK hidden Markov model based speech recognition toolkit. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 541-544.
  12. Samaria, I., & Harrell, D. (2005). Comparison of appearance-based face recognition methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 1953-1964.
  13. Nissenbaum, H. (2010). Privacy in the age of big data. Communications of the ACM, 53(9), 81-84.
  14. Zhang, H. J., & Suel, T. (2004). A survey of recent advancements in speaker recognition. IEEE Signal Processing Magazine, 21(6), 86-98.
  15. Chen, B., & Wang, Y. (2014). A survey of biometric authentication. ACM Computing Surveys (CSUR), 46(4), 1-29.
  16. Acquisti, A., & Gross, R. (2014). How much money is my data worth? A microeconomic analysis of personally identifiable information. Proceedings of the National Academy of Sciences, 111(5), 1780-1785.