  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.
