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.INDEX TERMS Biometrics, audio-visual person recognition, presentation attack detection.

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Well-known vulnerabilities of voice-based biometrics are impersonation, replay attacks, artificial signals/speech synthesis, and voice conversion. Among these, voice impersonation is the obvious and simplest way of attack that can be performed. Though voice impersonation by amateurs is considered not a severe threat to ASV systems, studies show that professional impersonators can successfully influence the performance of the voice-based biometrics system. In this work, we have created a novel voice impersonation attack dataset and studied the impact of voice impersonation on automatic speaker verification systems. The dataset consisting of celebrity speeches from 3 different languages, and their impersonations are acquired from YouTube. The vulnerability of speaker verification is observed among all three languages on both the classical i-vector based method and the deep neural network-based x-vector method.

Automatic Speaker Verification (ASV) systems accuracy is based on the spoken language used in training and enrolling speakers. Language dependency makes voice-based security systems less robust and generalizable to a wide range of applications. In this work, a study on language dependency of a speaker verification system and experiments are performed to benchmark the robustness of the x-vector based techniques to language dependency. Experiments are carried out on a smartphone multi-lingual dataset with 50 subjects containing utterances in four different languages captured in five sessions. We have used two world training datasets, one with only one language and one with multiple languages. Results show that performance is degraded when there is a language mismatch in enrolling and testing. Further, our experimental results indicate that the performance degradation depends on the language present in the word training data.