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

TL;DR

This paper introduces a cost-effective two-step biometric authentication system combining face recognition and voice verification, optimized for common devices, achieving high accuracy and robustness.

Contribution

The paper proposes a novel two-step biometric authentication pipeline that reduces computation and enhances robustness by sequentially using face and voice recognition.

Findings

Face recognition accuracy of 95.1% using pruned VGG-16.

Voice verification achieves 98.9% accuracy and 3.456% EER.

System is designed to be cost-effective with only a camera and microphone.

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.