RESEARCH OF INNOVATIVE AUTHENTICATION: A DEEP DIVE INTO BIOMETRIC ACCESS TECHNOLOGIES

As we navigate through the digital era, the scope of biometric authentication has significantly broadened, establishing itself as a cornerstone of modern security systems. This study explores the sophisticated methodologies and leading-edge technologies that are at the forefront of biometric access systems’ evolution. The transition from elemental techniques to advanced systems integrating facial recognition, fingerprint scanning, iris tracking, and additional modalities – each enhanced by artificial intelligence (AI) and machine learning (ML) – is thoroughly examined. A special focus is given to how the convergence of accuracy, speed, and user experience plays a crucial role in the broad acceptance of these technologies. The paper also delves deeper into the implications of biometric data processing, discussing the critical issues of security and privacy, as well as the ethical and regulatory challenges faced in deploying these technologies. Moreover, this discussion extends to the potential for these biometric systems to adapt to dynamic security threats, highlighting their resilience and flexibility in a rapidly evolving digital landscape.

1. Zhou Y. Evaluation of biometric recognition in the COVID-19 period, 2021 2nd International Conference on Computing and Data Science (CDS), Stanford, CA, USA, 2021, pp. 243-248. https://doi.org/10.1109/CDS52072.2021.00049

2. Belcher C. and Y. Du. A Selective Feature Information Approach for Iris Image-Quality Measure, in IEEE Transactions on Information Forensics and Security, Sept. 2008, vol. 3, no. 3, pp. 572–577. https://doi.org/10.1109/TIFS.2008.924606.

3. Abuhamad M., Abusnaina A., Nyang D. and D. Mohaisen. Sensor-Based Continuous Authentication of Smartphones’ Users Using Behavioral Biometrics: A Contemporary Survey, in IEEE Internet of Things Journal, Jan.1, 2021, vol. 8, no. 1, pp. 65–84, https://doi.org/10.1109/JIOT.2020.3020076.

4. Chakroun R. and M. Frikha. Robust Text-independent Speaker recognition with Short Utterances using Gaussian Mixture Models, 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, 2020, pp. 2204–2209. https://doi.org/10.1109/IWCMC48107.2020.9148102.

5. Sriman J., Thapar P., Alyas A.A. and U. Singh, Unlocking Security: A Comprehensive Exploration of Biometric Authentication Techniques, 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2024, pp. 136–141. https://doi.org/10.1109/Confluence60223.2024.10463322.

6. Nagendrarajah J. and M.U.S. Perera. Recognition of expression variant faces – a principle component analysis based approach for access control, 2010 IEEE International Conference on Information Theory and Information Security, Beijing, China, 2010, pp. 125-129, https://doi.org/10.1109/ICITIS.2010.5689611.

7. Prabhakar S., Pankanti S. and A.K. Jain. Biometric recognition: security and privacy concerns, in IEEE Security & Privacy, March-April 2003, vol. 1, no. 2, pp. 33–42. https://doi.org/10.1109/MSECP.2003.1193209.

8. Fairhurst M.C. and C. McIntosh. Assessing image characteristics for user feedback in biometric fingerprint identity verification tasks, IEE International Conference on Visual Information Engineering (VIE 2005), Glasgow, UK, 2005, pp. 1–6. https://doi.org/10.1049/cp:20050082.

9. Chen Y. et al. A High-Security EEG-Based Login System with RSVP Stimuli and Dry Electrodes, in IEEE Transactions on Information Forensics and Security, Dec. 2016, vol. 11, no. 12, pp. 2635–2647. https://doi.org/10.1109/TIFS.2016.2577551.

10. Se Young Chun. Single pulse ECG-based small scale user authentication using guided filtering, 2016 International Conference on Biometrics (ICB), Halmstad, 2016, pp. 1–7. https://doi.org/10.1109/ICB.2016.7550065.

11. Nakamura T., Goverdovsky V. and D.P. Mandic. In-Ear EEG Biometrics for Feasible and Readily Collectable Real-World Person Authentication, in IEEE Transactions on Information Forensics and Security, March 2018, vol. 13, no. 3, pp. 648–661. https://doi.org/ 10.1109/TIFS.2017.2763124.

12. Odinaka I., Lai P. -H., Kaplan A.D., O’Sullivan J.A., Sirevaag E.J. and J.W. Rohrbaugh, ECG Biometric Recognition: A Comparative Analysis, in IEEE Transactions on Information Forensics and Security, Dec. 2012, vol. 7, no. 6, pp. 1812–1824. https://doi.org/ 10.1109/TIFS.2012.2215324.

13. Jain A.K., Prabhakar S., Hong L. and S. Pankanti. Filterbank-based fingerprint matching, in IEEE Transactions on Image Processing, May 2000, vol. 9, no. 5, pp. 846–859. https://doi.org/10.1109/83.841531.

14. Jain A.K., Ross A. and S. Prabhakar. An introduction to biometric recognition, in IEEE Transactions on Circuits and Systems for Video Technology, Jan. 2004, vol. 14, no. 1, pp. 4–20. https://doi.org/10.1109/TCSVT.2003.818349.

15. Kumar A. and K. V. Prathyusha. Personal Authentication Using Hand Vein Triangulation and Knuckle Shape, in IEEE Transactions on Image Processing, Sept. 2009, vol. 18, no. 9, pp. 2127–2136. https://doi.org/10.1109/TIP.2009.2023153.

16. Kun Huang, Jiangyong Shi, Ming Xian and Jian Liu, Achieving robust biometric based access control mechanism for cloud computing, 2013 International Conference on Information and Network Security (ICINS 2013), Beijing, 2013, pp. 1–7, https://doi.org/10.1049/cp.2013.2471.

17. Bhargavi Devi P. and K. Sharmila. A Comparative Analysis of Deep-Learning Models with Novel Hybrid Biometric Modality Deep-Learning Network (BIOMODEN) to cognize Classification Accuracy of Fused Biometric Image, 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India, 2022, pp. 136–141, https://doi.org/10.1109/SMART55829.2022.10047758

18. Shu F., Zhang K., Luo C., Ma B. and S. Chen. Research on Security Protection Technology of Terminal Access Network Based on Fingerprint Perception and Identity Protection of IoT, 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China, 2020, pp. 680–683. https://doi.org/10.1109/ICIBA50161.2020.9277151.

19. Ceyhan E.B. and Ş. Sağiroğlu. Gender inference within Turkish population by using only fingerprint feature vectors, 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM), Orlando, FL, USA, 2014, pp. 146–150, https://doi.org/10.1109/CIBIM.2014.7015456.

20. Sathya K., Rajasekar V. and J. Premalatha. Biometric signcryption using hyperelliptic curve and cryptographically secure random number, 2016 International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, India, 2016, pp. 1–7. https://doi.org/10.1109/ICRTIT.2016.7569557.

21. Wang J. and G. Wang. Quality-Specific Hand Vein Recognition System, in IEEE Transactions on Information Forensics and Security, Nov. 2017, vol. 12, no. 11, pp. 2599–2610. https://doi.org/10.1109/TIFS.2017.2713340.

22. Ishfaq R., Selwal A. and D. Sharma. Fingerprint Spoofing Attacks and their Deep Learning-enabled Remediation: State-of-the-art, Taxonomy, and Future Directions, 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT), Sonepat, India, 2021, pp. 22–28, https://doi.org/10.1109/CCICT53244.2021.00016.

23. Yang W., Wang S., Sahri N., Karie N., Ahmed M., Valli C. Biometrics for Internet-of-Things Security: A Review. Sensors (Basel, Switzerland), 2021, 21. https://doi.org/10.3390/s21186163.

24. Malatji W., Eck R., Zuva T. Acceptance of Biometric Authentication Security Technology on Mobile Devices. 2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC), 2020, pp. 1–5. https://doi.org/10.1109/IMITEC50163.2020.9334082.