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 this https URL.
Abstract (translated)
我们提出了一种成本效益高的两步认证系统，该系统仅使用常见设备上配备的摄像头和麦克风，集成了面部识别和说话人验证。该管道首先执行面部识别以从一个小规模注册用户组中确定候选用户，然后只针对匹配的身份进行语音识别，以此来减少计算量并提高鲁棒性。对于面部识别，我们对一个由五个主体提供的924张图片的增强数据集训练了一个基于修剪后的VGG-16分类器，并通过MTCNN定位人脸；其准确率为95.1%。对于语音识别，我们在LibriSpeech（train-other-360）上训练了一个卷积神经网络说话人验证模型，在测试集test-clean上的准确性为98.9%，等错误率(EER)为3.456%。源代码和预训练模型可在此链接获取：[提供实际的URL地址]。
URL
https://arxiv.org/abs/2601.06218
