Room Security System Using Machine Learning with Face Recognition Verification

Abstract

Machine Learning (ML), an intelligent system known for its capacity to automate procedures by discerning patterns pertinent to specific tasks such as detection, prediction, and pattern recognition, is increasingly being used to advance biometric technologies. Among these, facial recognition, a subset of computer vision-based biometrics, is emerging as a robust security measure. The present study is centered on the design of a room security system that leverages facial recognition, rooted in a Convolutional Neural Network (CNN) architecture. The CNN model was constructed within the Tensorflow framework, employing the Keras library and Scikit-learn, all embedded within a Raspberry Pi system. The model was trained on 15 registered face classes, with an additional three unregistered classes used for biometric security testing. Performance was evaluated using the False Acceptance Rate (FAR) and False Rejection Rate (FRR), metrics that assess the system's ability to accurately verify authorized and unauthorized users. Findings demonstrated that the CNN model achieved a 97% accuracy rate in facial identification. Furthermore, biometric security testing of the CNN model using room security devices yielded optimal results at a threshold of 90%, with FAR=26.67%, FRR=9.33%, and an Equal Error Rate (EER) of 21.33%. It was observed that factors such as lighting, data variation, resolution, and positional changes during data sampling could impact the system's performance in real-time operations. It is therefore recommended that data collection and facial scanning be consistently conducted under identical environmental conditions to enhance the accuracy of the system. This study signifies a substantial stride in the development of advanced room security systems, thus contributing to the broader realm of secure access control systems.