Facial Recognition-Based Entry System for Student Residence Halls: Enhancing Security and Accessibility

Abstract

Protecting an organization from numerous threats both inside and outside is the primary function of the security system. Automated embedded systems have come a long way in the contemporary era and have shown to be highly beneficial in applications related to security and surveillance. Face recognition is one of the study fields in computer vision, which is commonly used in security systems for video surveillance. Even though facial recognition technology has advanced significantly and is employed in several significant applications, numerous challenges need to be solved. These challenges include changes in posture, occlusions, expression, aging, lighting, and other elements. Deep learning can be useful in these situations. By using several processing layers to develop data representations with multiple feature extraction layers, deep learning may achieve higher accuracy. With the purpose of providing better security for student residence halls, in this work, we present a real-time deep learning-based facial recognition system that can be used to identify an individual's identity and give a warning when the individual's face is not recognized in front of the system. Here, faces from the face database are matched in order to identify students based on a video of their arrival into the residence halls. This process begins with face detection and ends with face recognition. We used a Convolutional Neural Network (CNN) based model Multi-Task Cascaded Convolutional Neural Networks (MTCNN) for face detection and recognizing faces using the Google FaceNet model. The model was trained on around 3000 photos, taken by 30 distinct people.