Two-step Authentication: Multi-biometric System Using Voice and Facial Recognition
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908756824031232 |
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| author | Chen, Kuan Wei Lin, Ting Yi Yang, Wen Ren Kesarwani, Aryan Singh, Riya |
| author_facet | Chen, Kuan Wei Lin, Ting Yi Yang, Wen Ren Kesarwani, Aryan Singh, Riya |
| contents | We present a cost-effective two-step authentication system that integrates face identification and speaker verification using only a camera and microphone available on common devices. The pipeline first performs face recognition to identify a candidate user from a small enrolled group, then performs voice recognition only against the matched identity to reduce computation and improve robustness. For face recognition, a pruned VGG-16 based classifier is trained on an augmented dataset of 924 images from five subjects, with faces localized by MTCNN; it achieves 95.1% accuracy. For voice recognition, a CNN speaker-verification model trained on LibriSpeech (train-other-360) attains 98.9% accuracy and 3.456% EER on test-clean. Source code and trained models are available at https://github.com/NCUE-EE-AIAL/Two-step-Authentication-Multi-biometric-System. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06218 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Two-step Authentication: Multi-biometric System Using Voice and Facial Recognition Chen, Kuan Wei Lin, Ting Yi Yang, Wen Ren Kesarwani, Aryan Singh, Riya Computer Vision and Pattern Recognition Artificial Intelligence Multimedia I.5.4; I.4.8 We present a cost-effective two-step authentication system that integrates face identification and speaker verification using only a camera and microphone available on common devices. The pipeline first performs face recognition to identify a candidate user from a small enrolled group, then performs voice recognition only against the matched identity to reduce computation and improve robustness. For face recognition, a pruned VGG-16 based classifier is trained on an augmented dataset of 924 images from five subjects, with faces localized by MTCNN; it achieves 95.1% accuracy. For voice recognition, a CNN speaker-verification model trained on LibriSpeech (train-other-360) attains 98.9% accuracy and 3.456% EER on test-clean. Source code and trained models are available at https://github.com/NCUE-EE-AIAL/Two-step-Authentication-Multi-biometric-System. |
| title | Two-step Authentication: Multi-biometric System Using Voice and Facial Recognition |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Multimedia I.5.4; I.4.8 |
| url | https://arxiv.org/abs/2601.06218 |