Face Recognition for Kindergarten Security Application

Chan, Wei Heng (2023) Face Recognition for Kindergarten Security Application. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

The security of schools and educational institutions is an essential aspect of ensuring the safety of children and staff. With the advancements in biometric recognition technology, facial recognition systems have gained tractions in various commercial applications. Therefore, this project aims to design and develop an active security system specifically for kindergarten context, which integrates facial recognition technology with a solenoid lock and LED visual indicator. The face recognition algorithm used will be based on the FaceNet neural network model, which is implemented through the face_recognition Python library developed by Adam Geitgey. The study will employ a mixed-methods research approach, including a literature review, system design, prototype development, and user testing. The prototype’s effectiveness, accuracy, and reliability will be evaluated based on various metrics such as false positives, false negatives, and response time. The limitations acknowledged in this study include the performance of the face recognition system that may be limited by the processing power of the hardware used to test it and privacy concerns. The prototype’s design will also take into account potential privacy concerns related to the deployment of facial recognition technology in educational settings. For example, the system will store only the necessary facial data and will ensure that the data is encrypted and protected from unauthorised access. The findings of this study have significant implications for the adoption of facial recognition-based security systems in Malaysian educational institutions. The system’s successful implementation could serve as a model for other schools in the country and contribute to enhancing school security. Moreover, the study’s limitations and findings can help inform policymakers and decision-makers in developing regulations and guidelines for the deployment of facial recognition technology in educational settings.