Automatic Quality Assessment for Audio-Visual Verification Systems. The LOVe submission to NIST SRE Challenge 2019

Antipov, Grigory; Gengembre, Nicolas; Blouch, Olivier Le; Lan, Gaël Le

doi:10.48550/arxiv.2008.05889

Cited by 1 publication

(1 citation statement)

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“…Quality assessment based score-level fusion was performed by Antipov et al [11] for Audio-Visual speaker verification. For face recognition, four face embeddings, namely ResNet-50, PyramidNet, ArcFace-50, ArcFace-100, are aggregated using a Transformer aggregation model.…”

Section: Post-mapping or Late Fusionmentioning

confidence: 99%

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

Mandalapu,

Reddy,

Ramachandra

et al. 2021

Preprint

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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.

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Section: Post-mapping or Late Fusionmentioning

confidence: 99%