AI-Based Biometric Authentication

By: Achit Katiyar1

1International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan. Email: achitktr@gmail.com

Abstract: Biometric authentication using artificial intelligence or AI has turned out to be an authentic means of identity verification in both personal and enterprise systems. When the results of pattern recognition based on AI and machine learning are integrated into the biometric modalities of fingerprints, facial recognition, iris scans, voice recognition procedures, the authenticity of the process becomes quicker, more efficient, and less vulnerable to fraud. In this paper, the authors have tried to present Artificial Intelligence Biometric Authentication their genesis and their current scenarios along with significant technologies involved in them and their benefits, shortcomings and future scope.

Introduction

AI-enabled biometric authentication is gradually becoming the new face of security systems in various industries using biological features for people’s Identification. This technology uses artificial intelligence (AI) to improve the efficiency to operate biometric systems, issues like insecurity and the user interface. Since AI has the characteristics of learning and has become better and better with data, biometric systems are no longer limited and can face various problems, for example, changes in appearance or environmental conditions. This paper aims to show how AI can improve biometric authentication with an emphasis on technologies in use today, the current uses of AI in biometric authentication, and the implications for the future.

Biometric Authentication Technologies

Biometric authentication technologies are based upon the use of specific personal characteristics to provide authentication, and are more secure and convenient for the end user. These systems have undergone a drastic change and now they are blended with enhanced algorithmic and AI components [1]. The potential of biometrics for authenticating individuals is explained below and underlined by key elements of biometric authentication technologies.

  • Key Biometric Techniques
  1. Facial Recognition: Employing algorithms, it establishes a reference framework for identifying someone by matching distinct face attributes [2], [3].
  2. Fingerprint Recognition: That is fast and accurate, but has its special patterns on fingers, allowing it to be employed in different applications [4].
  3. Iris Recognition: Is the safest since eyeball’s patterns, known as the irises, are different in every individual and do not alter over the course of human’s life [4].
Figure 1: Biometric authentication technologies

The Role of AI in Biometric Authentication

The incorporation of artificial intelligence in biometric authentication is changing the landscape of security within sectors including finance and digital transactions. AI improves aspects of biometric systems that are hard to implement by traditional methods making them more secure than other methods.

  • Enhanced Security Measures
  1. AI-based applications of biometric are the fingerprint, facial, and voice recognition that eliminate the possibility of intruders gaining access [5].
  2. The use of machine learning may help uncover biometric data anomalies and alert against spoofing attacks [6], [7].
  • Improved User Experience
  1. The implemented biometric authentication reduces user interactions with the system to a simplified form, thus reducing complexity as well as the errors inherent in memory based methods such as passwords [5], [7].
  2. AI can learn from user habits timely and assist in preparation for transactions before they are made, which contributes to increased efficiency [6].

Despite the many benefits that biometric systems initiated with the use of AI present, several hurdles like data privacy and the need to safely store the biometric details remain an issue of contention that must be met to allow users to fully engage with the system.

Challenges and Limitations

  • Vulnerabilities to Attacks:
  1. Biometric systems with AI are vulnerable to spoofing and adversarial attacks, which can lead to their being hacked, [8].
  2. External physiological factors, including fingerprints and facial identification, are vulnerable to fraud and are, therefore, questionable in practical use [9].
  • Ethical and Legal Issues Including Data Privacy:
  1. Collection and storage of biometric data present a high risk to privacy and therefore requires strong data protection practices and conformity to the set regulations [8].
  2. Ethical implications arise from the potential misuse of biometric data, particularly in surveillance and tracking scenarios [10].
  • Technological Limitations:
  1. Conventional biometric systems have a number of disadvantages due to which results in wrong identification especially when there is a change in environment [9].
  2. The application of AI in keystroke dynamics as a part of behavioural biometrics demands the secure storage and processing of users’ data for improving the recognition results and reducing possible risks [11].

Nevertheless, the possibilities for the enhancement of biometric systems through the integration of AI are still promising, and indicate that more work should be done to effectively address these threats.

Conclusion

Biometric verification using Artificial Intelligence is changing the nature of how people and companies protect their accounts. With the help of AI’s inherent qualities that consist of pattern recognition, deep learning, and machine learning, biometric systems enhance in their precision, speed, and security. Nevertheless, the questions of privacy, bias, and spoofing are still open. Prospects in the field of AI and multimodal biometrics that has developed in recent years appears to be able to solve these problems and offer more reliable protection tools in the future.

References

  1. S. Manikandan, M. Rahaman, and Y.-L. Song, “Active Authentication Protocol for IoV Environment with Distributed Servers,” Comput. Mater. Contin., vol. 73, no. 3, pp. 5789–5808, 2022, doi: 10.32604/cmc.2022.031490.
  2. S. Singh, S. Sharma, M. Awasthi, S. Rawat, and Y. Chanti, “Advancements of Emerging Technologies in Biometrics Authentication,” in 2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC), Jan. 2024, pp. 1–7. doi: 10.1109/KHI-HTC60760.2024.10482030.
  3. J. S. K, “Automatic Biometric Recognition,” IJFMR – Int. J. Multidiscip. Res., vol. 6, no. 1, doi: 10.36948/ijfmr.2024.v06i01.13236.
  4. “Comparative Analysis of Different Biometric Techniques for Security Systems,” Aust. J. Eng. Innov. Technol., pp. 141–153, Jun. 2023, doi: 10.34104/ajeit.023.01410153.
  5. productioneditor, “AI-driven biometrics for secure fintech: Pioneering safety and trust,” International Journal of Engineering Research Updates. Accessed: Sep. 22, 2024. [Online]. Available: https://orionjournals.com/ijeru/content/ai-driven-biometrics-secure-fintech-pioneering-safety-and-trust
  6. A. K. Ganguly, S. Bhattacharya, and S. Chattopadhyay, “A Design of Efficient Biometric based Banking System Through AI-Powered Transaction Security Fintech System for Secure Transactions,” in 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), May 2024, pp. 492–496. doi: 10.1109/ICACITE60783.2024.10617391.
  7. A. Tandon, C. Anitha, A. Kataria, N. Q. Mohammed, M. Y. Al-Khuzaie, and A. A. Almulla, “Allometry Authentication in the Field of Finance: Creation of Well Secured System using AI Algo Based Systems,” in 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), May 2024, pp. 962–967. doi: 10.1109/ICACITE60783.2024.10617124.
  8. U. H Patel and K. Gera, “Biometric Security Systems Enhanced by AI: Exploring Concerns with AI Advancements in Facial Recognition and Other Biometric Systems have Security Implications and Vulnerabilities,” Int. J. Innov. Sci. Res. Technol. IJISRT, pp. 2078–2082, Jul. 2024, doi: 10.38124/ijisrt/IJISRT24JUN1510.
  9. S. Madduluri and T. K. Kumar, “Priority-based Multi-feature Vector Model Using Convolution Neural Network for Biometric Authentication,” Int. J. Comput. Intell. Syst., vol. 17, no. 1, p. 136, Jun. 2024, doi: 10.1007/s44196-024-00533-5.
  10. M. Rüb, J. Herbst, C. Lipps, and H. D. Schotten, “No One Acts like You: AI based Behavioral Biometric Identification,” in 2022 3rd International Conference on Next Generation Computing Applications (NextComp), Oct. 2022, pp. 1–7. doi: 10.1109/NextComp55567.2022.9932247.
  11. B. Tural, Z. Örpek, and S. Özmen, “Artificial Intelligence and Keystroke Dynamics: The Mysterious World of Personal Signatures,” in 2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), May 2024, pp. 1–5. doi: 10.1109/HORA61326.2024.10550804.

Cite As

Katiyar A. (2024) AI-Based Biometric Authentication, Insights2Techinfo, pp.1

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