Design and software development of a camera-integrated access control device which has legal evidence qualifications

This thesis focuses on the design, implementation, and evaluation of a camera-based access control system. These systems play a significant role in security and surveillance by controlling and monitoring entries and exits, thereby enhancing security and providing monitoring capabilities. In this study, an integrated access control system using an NFC card reader (RC522 module), a camera module, and a Raspberry Pi has been developed. The system design successfully integrates hardware components and develops software components. Facial recognition processes have been performed using the Python programming language and the OpenCV library, while data has been securely stored using a PostgreSQL database. A central management software has been developed using the Laravel framework. Data transfer in JSON format has facilitated data integration and ensured compatible data communication between different system components. Performance and reliability tests have shown that the system achieves high accuracy rates, securely stores data, and ensures data integrity with timestamping. The validity and reliability of electronic evidence have been evaluated within the framework of Law No. 5651 and relevant regulations, confirming that the system meets legal requirements. To enhance user experience, management features have been developed through SSH and web interfaces, allowing users to easily change system settings, view access logs, and monitor system status. Usability tests have shown that user interfaces are user-friendly. Improvements made based on feedback from test users have increased the system's usability and efficiency. In conclusion, the camera-based access control system developed in this thesis offers a reliable, effective, and user-friendly solution, making a significant contribution to the field of security. The system is suitable for real-world applications and can be effectively used in security and surveillance processes. It is anticipated that the system can be further developed in the future with the integration of artificial intelligence and machine learning technologies, improving the accuracy of facial recognition algorithms. Additionally, integrating the system with Internet of Things (IoT) technologies can provide a more comprehensive and holistic security solution. This study offers a high-standard solution in terms of security by meeting both technical and legal requirements.