Abstract:
Biometrics, involving the analysis of human behavior and characteristics, has become
instrumental in enhancing security measures. This paper explores the various
approaches employed in face recognition, a pivotal biometric technique. The survey
conducted here evaluates different algorithms and processes, positioning face
recognition as a novel biometric division with promising applications in healthcare
security. The focus shifts to the utilization of face detection methods with computer
assistance. Shifting gears, the paper delves into a general theory overview,
juxtaposing behavioral and neural phenomena in face recognition against object
recognition. Original experiments are presented to test key assumptions, highlighting
the need to discern and apply Convolutional Neural Network (CNN). The overarching
goal is to revolutionize traditional security systems in educational institutions and
restricted areas. This proposed system aims to streamline verification processes,
significantly reducing the time required for traditional campus security measures. The
system excels at identifying individuals not registered in the database or unauthorized
personnel, thereby fortifying office security against potential thefts. As the research
unfolds, it becomes evident that this innovative approach to security stands as a
beacon of efficiency, offering a swift and reliable means of verification with
substantial implications for theft prevention and enhanced overall security. Our
achieved accuracy is 96.5% in face recognition.
