Enhancing Mobile Biometric Authentication: A New Model for Addressing Security Vulnerabilities in Face ID Technology

• Author(s): Sikirat Damilola Mustapha ; Abidemi Adeleye Alabi
• Paper ID: 1705591
• Page: 352-372
• Published Date: 31-03-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 9 March-2024

Abstract

Face ID technology has become a cornerstone of mobile biometric authentication, offering convenience and enhanced user experience. However, its increasing adoption has also highlighted critical security vulnerabilities, such as spoofing attacks, deepfake exploitation, and issues with environmental adaptability. This study presents a novel model aimed at addressing these vulnerabilities to strengthen the reliability and security of Face ID technology. The proposed model integrates advanced machine learning algorithms with multi-factor biometric authentication to enhance the robustness of facial recognition systems. Key features include real-time liveness detection, anti-spoofing measures, and adaptive recognition capabilities that improve accuracy across diverse environments and demographics. The model employs a hybrid approach, combining traditional facial recognition methods with supplementary biometric indicators, such as eye movement patterns and thermal imaging, to mitigate potential attack vectors. This research employs a mixed-methods approach, including simulated attack scenarios, user trials, and algorithmic performance assessments. Results demonstrate that the new model significantly reduces the success rate of spoofing attempts and deepfake breaches while maintaining high authentication speed and user convenience. The study also highlights the model's adaptability to low-light and high-motion conditions, addressing longstanding limitations in current Face ID systems. The findings underscore the importance of incorporating multi-layered security mechanisms in biometric authentication technologies to balance user experience with robust security. Furthermore, this model paves the way for future innovations in mobile authentication, promoting safer and more inclusive digital ecosystems. Policy implications include advocating for industry-wide adoption of enhanced biometric standards and establishing guidelines for integrating advanced security features into consumer-grade devices. Future research could explore the scalability of the model and its application in other sectors, such as healthcare and finance, where secure and efficient authentication is paramount.

Keywords

Face ID Technology, Mobile Biometric Authentication, Security Vulnerabilities, Liveness Detection, Anti-Spoofing Measures, Multi-Factor Authentication, Deepfake Exploitation, Environmental Adaptability, Biometric Security, Facial Recognition.

Citations

IRE Journals:
Sikirat Damilola Mustapha , Abidemi Adeleye Alabi "Enhancing Mobile Biometric Authentication: A New Model for Addressing Security Vulnerabilities in Face ID Technology" Iconic Research And Engineering Journals Volume 7 Issue 9 2024 Page 352-372

IEEE:
Sikirat Damilola Mustapha , Abidemi Adeleye Alabi "Enhancing Mobile Biometric Authentication: A New Model for Addressing Security Vulnerabilities in Face ID Technology" Iconic Research And Engineering Journals, 7(9)