Obscure face recognition using deep neural networks

Abstract

One of the most effective biometric tools for securing diverse systems is facial recog- nition technology. Traditional security mechanisms like PINs, passwords, and fin- gerprints have shown to be less effective and dependable than this technology. Sys- tems for surveillance, finance, and security have all made substantial use of facial recognition technology. But facial recognition technology is currently facing a huge challenge from the COVID-19 pandemic. Due to face occlusion brought on by the widespread use of masks, it is now challenging to precisely identify people. This problem has motivated a number of academics to develop facial recognition tech- nology that is more accurate by focusing on hidden facial features. In this paper, we provide a sophisticated method for identifying faces even when there are facial alterations or masks are used. The CASIA, VGG2, LFW Databases are used to help us build our technique, which entails figuring out which facial traits are still discernible even when the face is partially hidden. Convolutional neural networks (CNNs), which are the foundation of our method and are used to extract perti- nent facial information, are based on deep learning techniques. We contrasted our findings with those of other cutting-edge facial recognition systems to assess our strategy. By obtaining more accuracy and speed, our system outperformed other systems. We got 95.85% accuracy in face recognition with the model Inception- ResNetV1 by using ArcFace loss function. Also got 94.28% accuracy on the same model by using Triplet loss function. We worked on the never before worked model for face recognition which is SE-ResNeXt-101. We also got 93.41% accuracy on that model with ArcFace loss function. That indicates that our research underlines the significance of creating environmental change-resistant facial recognition