Two-step Authentication: Multi-biometric System Using Voice and Facial Recognition
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.
Cite
@article{arxiv.2601.06218,
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},
journal= {arXiv preprint arXiv:2601.06218},
year = {2026}
}
Comments
Accepted manuscript (author version, v2). The published version appears in IET Conference Proceedings; see DOI: 10.1049/icp.2024.4141. Code: https://github.com/NCUE-EE-AIAL/Two-step-Authentication-Multi-biometric-System