Security-oriented Face Detection Technology Utilizing Deep Learning Techniques Along with the CASIA Datasets

Abstract

Recently, face recognition technology has become increasingly important for safety purposes. Masks are now required in most countries and are increasingly used. Public health professionals advise people to conceal their facial features outdoors to reduce COVID-19 transmission by 65%. Detecting people without masks on their faces is crucial. This has become widely used as face recognition outperforms PINs, passwords, fingerprints, and other safety verification methods. Sunglasses, scarves, caps, and makeup have made facial identification harder in recent decades. Thus, such masks impact facial recognition performance. Face masks also make traditional technology for facial recognition ineffective for face authorization, security checks, school monitoring, and cellphone and laptop opening. Thus, we proposed Masked Facial Recognition (MFR) to recognize veiled and exposed-face people so they don't need to remove their masks to verify themselves. This deep computing model was trained with Inception Res Network V1. CASIA is responsible for preparing pictures and using LFW to validate models. Dlib creates masked datasets utilizing vision algorithms. About 96% accuracy was achieved using our three models that were trained. Thus, covered and uncovered recognition of faces and detection techniques in security and safety verification might easily be used. These systems can be used in various settings, such as airports, train stations, and other public places, to enhance security and prevent crime. Overall, deep learning within face recognition technology has significant potential for improving safety and security in various settings.