Multilingual Audio-Visual Smartphone Dataset and Evaluation
Smartphones have been employed with biometric-based verification systems to provide security in highly sensitive applications. Audio-visual biometrics are getting popular due to their usability, and also it will be challenging to spoof because of their multimodal nature. In this work, we present an...
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IEEE
2021
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oai:doaj.org-article:884f705a0b474dd0b570d8f728e897112021-11-20T00:01:13ZMultilingual Audio-Visual Smartphone Dataset and Evaluation2169-353610.1109/ACCESS.2021.3125485https://doaj.org/article/884f705a0b474dd0b570d8f728e897112021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9600884/https://doaj.org/toc/2169-3536Smartphones have been employed with biometric-based verification systems to provide security in highly sensitive applications. Audio-visual biometrics are getting popular due to their usability, and also it will be challenging to spoof because of their multimodal nature. In this work, we present an audio-visual smartphone dataset captured in five different recent smartphones. This new dataset contains 103 subjects captured in three different sessions considering the different real-world scenarios. Three different languages are acquired in this dataset to include the problem of language dependency of the speaker recognition systems. These unique characteristics of this dataset will pave the way to implement novel state-of-the-art unimodal or audio-visual speaker recognition systems. We also report the performance of the bench-marked biometric verification systems on our dataset. The robustness of biometric algorithms is evaluated towards multiple dependencies like signal noise, device, language and presentation attacks like replay and synthesized signals with extensive experiments. The obtained results raised many concerns about the generalization properties of state-of-the-art biometrics methods in smartphones.Hareesh MandalapuP. N. Aravinda ReddyRaghavendra RamachandraKrothapalli Sreenivasa RaoPabitra MitraS. R. Mahadeva PrasannaChristoph BuschIEEEarticleSmartphone biometricsaudio-visual speaker recognitionpresentation attack detectionmultilingualElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 153240-153257 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Smartphone biometrics audio-visual speaker recognition presentation attack detection multilingual Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
| spellingShingle |
Smartphone biometrics audio-visual speaker recognition presentation attack detection multilingual Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hareesh Mandalapu P. N. Aravinda Reddy Raghavendra Ramachandra Krothapalli Sreenivasa Rao Pabitra Mitra S. R. Mahadeva Prasanna Christoph Busch Multilingual Audio-Visual Smartphone Dataset and Evaluation |
| description |
Smartphones have been employed with biometric-based verification systems to provide security in highly sensitive applications. Audio-visual biometrics are getting popular due to their usability, and also it will be challenging to spoof because of their multimodal nature. In this work, we present an audio-visual smartphone dataset captured in five different recent smartphones. This new dataset contains 103 subjects captured in three different sessions considering the different real-world scenarios. Three different languages are acquired in this dataset to include the problem of language dependency of the speaker recognition systems. These unique characteristics of this dataset will pave the way to implement novel state-of-the-art unimodal or audio-visual speaker recognition systems. We also report the performance of the bench-marked biometric verification systems on our dataset. The robustness of biometric algorithms is evaluated towards multiple dependencies like signal noise, device, language and presentation attacks like replay and synthesized signals with extensive experiments. The obtained results raised many concerns about the generalization properties of state-of-the-art biometrics methods in smartphones. |
| format |
article |
| author |
Hareesh Mandalapu P. N. Aravinda Reddy Raghavendra Ramachandra Krothapalli Sreenivasa Rao Pabitra Mitra S. R. Mahadeva Prasanna Christoph Busch |
| author_facet |
Hareesh Mandalapu P. N. Aravinda Reddy Raghavendra Ramachandra Krothapalli Sreenivasa Rao Pabitra Mitra S. R. Mahadeva Prasanna Christoph Busch |
| author_sort |
Hareesh Mandalapu |
| title |
Multilingual Audio-Visual Smartphone Dataset and Evaluation |
| title_short |
Multilingual Audio-Visual Smartphone Dataset and Evaluation |
| title_full |
Multilingual Audio-Visual Smartphone Dataset and Evaluation |
| title_fullStr |
Multilingual Audio-Visual Smartphone Dataset and Evaluation |
| title_full_unstemmed |
Multilingual Audio-Visual Smartphone Dataset and Evaluation |
| title_sort |
multilingual audio-visual smartphone dataset and evaluation |
| publisher |
IEEE |
| publishDate |
2021 |
| url |
https://doaj.org/article/884f705a0b474dd0b570d8f728e89711 |
| work_keys_str_mv |
AT hareeshmandalapu multilingualaudiovisualsmartphonedatasetandevaluation AT pnaravindareddy multilingualaudiovisualsmartphonedatasetandevaluation AT raghavendraramachandra multilingualaudiovisualsmartphonedatasetandevaluation AT krothapallisreenivasarao multilingualaudiovisualsmartphonedatasetandevaluation AT pabitramitra multilingualaudiovisualsmartphonedatasetandevaluation AT srmahadevaprasanna multilingualaudiovisualsmartphonedatasetandevaluation AT christophbusch multilingualaudiovisualsmartphonedatasetandevaluation |
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