Audio-Visual Biometric Recognition and Presentation Attack Detection: A Comprehensive Survey

TL;DR

This survey reviews the latest audio-visual biometric recognition methods, publicly available datasets, and attack detection techniques, highlighting challenges and open problems in enhancing security and robustness in real-world applications.

Contribution

It provides a comprehensive overview of current audio-visual biometric recognition and presentation attack detection techniques, including datasets and challenges, which was lacking in existing literature.

Findings

Audio-visual integration improves recognition accuracy.

Public datasets facilitate benchmarking and research.

Detection of presentation attacks remains a significant challenge.

Abstract

Biometric recognition is a trending technology that uses unique characteristics data to identify or verify/authenticate security applications. Amidst the classically used biometrics, voice and face attributes are the most propitious for prevalent applications in day-to-day life because they are easy to obtain through restrained and user-friendly procedures. The pervasiveness of low-cost audio and face capture sensors in smartphones, laptops, and tablets has made the advantage of voice and face biometrics more exceptional when compared to other biometrics. For many years, acoustic information alone has been a great success in automatic speaker verification applications. Meantime, the last decade or two has also witnessed a remarkable ascent in face recognition technologies. Nonetheless, in adverse unconstrained environments, neither of these techniques achieves optimal performance. Since audio-visual information carries correlated and complementary information, integrating them into one recognition system can increase the system's performance. The vulnerability of biometrics towards presentation attacks and audio-visual data usage for the detection of such attacks is also a hot topic of research. This paper made a comprehensive survey on existing state-of-the-art audio-visual recognition techniques, publicly available databases for benchmarking, and Presentation Attack Detection (PAD) algorithms. Further, a detailed discussion on challenges and open problems is presented in this field of biometrics.