Two-step Authentication: Multi-biometric System Using Voice and Facial Recognition
This work addresses authentication security for users of common devices, but it is incremental as it integrates existing methods in a novel pipeline.
The paper tackles user authentication by proposing a two-step system that combines face and voice recognition, achieving 95.1% accuracy for face identification and 98.9% accuracy with 3.456% EER for speaker verification.
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.