Related papers: Deep Learning-based Spatio Temporal Facial Feature Visual Speech Recognition

Deep Learning-based Spatio Temporal Facial Feature Visual Speech Recognition

URL: http://arxiv.org/abs/2305.00552v1
Date: Sun, 30 Apr 2023 18:52:29 GMT
Title: Deep Learning-based Spatio Temporal Facial Feature Visual Speech Recognition
Authors: Pangoth Santhosh Kumar, Garika Akshay
Abstract summary: 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. The suggested model attained an accuracy of 96.1% when tested on the industry-standard MIRACL-VC1 dataset.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.