Biometric Authentication in Android: Enhancing Security with AI-Powered Solutions

Abstract

Aims: This study aims to analyze biometric authentication methods on the Android platform, focusing on enhancing security through ready-to-use AI solutions. The research evaluates existing biometric authentication techniques, their vulnerabilities, and the application of AI-driven approaches to mitigate security risks. Study Design: This is a review and analytical study that examines current biometric authentication mechanisms, AI-based enhancements, and their impact on security and accuracy. Place and Duration of Study: The study is based on literature review and practical analysis of AI-enhanced biometric authentication methods applied in real-world Android applications. Methodology: The research explores the evolution of biometric authentication in Android, emphasizing the use of AI-driven tools such as ML Kit for Face Detection, TensorFlow Lite, and OpenCV. The study assesses the effectiveness of these technologies in improving recognition accuracy, reducing false acceptance and rejection rates, and addressing security threats such as spoofing attacks. Performance metrics, including False Acceptance Rate (FAR), False Rejection Rate (FRR), and processing time, were used to evaluate AI-enhanced solutions. Results: The findings indicate that AI-based enhancements significantly reduce the FAR by 15–20%, improving the overall reliability of biometric authentication. Machine learning models and image preprocessing techniques help adapt authentication to varying conditions, such as poor lighting and occlusions. However, AI integration introduces increased computational overhead, slightly extending processing time from 500ms to 700–800ms. Hardware-backed security measures mitigate risks associated with biometric data storage and manipulation. Conclusion: AI-driven biometric authentication methods substantially improve security and accuracy on Android devices, addressing key vulnerabilities in traditional biometric techniques. Despite minor processing time increases, the trade-off is justified by enhanced protection against spoofing attacks and improved adaptability to environmental conditions. Future research should focus on optimizing AI models for mobile efficiency and developing multi-factor authentication approaches to further enhance security.