Machine vision based Facial Recognition system with Database Management System for Gate Access Control and Attendance System

Abstract

During the last two decades' security of men and materials remains a vital concern in the whole world. In particular, Pakistan being a victim of multiple terrorist attacks on military and civil setups suffered massive damage. To encounter a security threat, a lot of efforts have been done to improve the organization's security and various enhanced safety checks were incorporated. Moreover, with the emergence of novel coronavirus pandemic, face masks have become an important part of daily routine life. Nearly every organization in the world has adopted face masks as a primary precautionary measure to secure their workplaces. Masked faces have made existing technology ineffective in several scenarios, such as facial recognition access control and facial security checks at public places This presents a new challenge to any organization’s security. Since timely identification of masked faces is vital for an organization. For any setup to ensure right entry at the gate with an automated system for human face recognition (even with mask) in a real-time background is the latest requirement. A face recognition system is an application of computer vision that can perform two tasks identifying and verifying a person from a given database, intending to reduce the manual efforts of the management and security staff. This research thus focuses on implementing a face recognition system (even with a face mask) by using a machine vision-based approach. A dataset of masked faces was collected to train the Support Vector Machine classifier on state-of-the-art Facial Recognition Feature Extractor Convolution Neural Network. Proposed Methodology gives recognition accuracy of 98% with masked faces, and results in an effective gate access control and attendance system.