Two-step Authentication: Multi-biometric System Using Voice and Facial Recognition

Kuan Wei Chen, Ting Yi Lin, Wen Ren Yang, Aryan Kesarwani, Riya Singh
1/9/2026
cs.CVcs.AIcs.MM

Official code: NCUE-EE-AIAL/Two-step-Authentication-Multi-biometric-System (9 stars, last commit 2026-03-17).

Abstract

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.

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Code Implementations(1)

NCUE-EE-AIAL/Two-step-Authentication-Multi-biometric-SystemOfficial100%
91Python, Jupyter NotebookJul 16, 20247 months ago

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Cite this paper

@article{chen2026twostep,
  title  = {Two-step Authentication: Multi-biometric System Using Voice and Facial Recognition},
  author = {Kuan Wei Chen and Ting Yi Lin and Wen Ren Yang and Aryan Kesarwani and Riya Singh},
  year   = {2026},
  eprint = {2601.06218},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.06218v1}
}

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