In low-resource computing contexts, such as smartphones and other tiny devices, Both deep learning and machine learning are being used in a lot of identification systems. as authentication techniques. The transparent, contactless, and non-invasive nature of these face recognition technologies driven by AI has led to their meteoric rise in popularity in recent years. While they are mostly successful, there are still methods to get inside without permission by utilising things like pictures, masks, glasses, etc. In this research, we present an alternate authentication process that makes use of both facial recognition and the individual's distinctive temporal facial feature motions while they speak a password. Because the suggested methodology allows for a password to be specified in any language, it is not limited by language. The suggested model attained an accuracy of 96.1% when tested on the industry-standard MIRACL-VC1 dataset, demonstrating its efficacy as a reliable and powerful solution. In addition to being data-efficient, the suggested technique shows promising outcomes with as little as 10 positive video examples for training the model. The effectiveness of the network's training is further proved via comparisons with other combined facial recognition and lip reading models.

翻译：在低资源计算环境下，如智能手机和其他微型设备，深度学习和机器学习正被广泛应用于诸多识别系统作为身份认证技术。这些由人工智能驱动的面部识别技术因其透明、非接触、无侵入的特性，近年来迅速普及。虽然它们大多行之有效，但仍存在通过使用照片、面具、眼镜等手段未经授权进入系统的方法。在本研究中，我们提出了一种替代性身份认证流程，该流程结合了面部识别与个体在说出密码时的独特时空面部特征运动。由于所提出的方法允许使用任意语言指定密码，因此不受语言限制。该模型在行业标准MIRACL-VC1数据集上测试时达到了96.1%的准确率，证明其作为可靠高效解决方案的有效性。此外，所提出的技术具有数据高效性，在仅使用10个正面视频示例训练模型时即显示出令人满意的效果。通过与其他面部识别与唇读组合模型的比较，进一步验证了该网络训练的有效性。