<![CDATA[The security of weapon storage warehouses is a critical concern that requires an access control system with exceptionally high reliability, particularly in minimizing false acceptance, where unauthorized individuals are incorrectly granted access. In high-risk facilities, even a single false acceptance incident can lead to serious security consequences. Conventional systems based on physical keys or access cards present limitations, including risks of loss, duplication, and access forgery. Therefore, a biometric-based solution is necessary to enhance identification accuracy and strengthen overall security. This study aims to design and implement a reliable, high-security facial-recognition-based access control system for weapon storage facilities. The proposed system integrates a Multi-task Cascaded Convolutional Neural Network (MTCNN) for face detection, FaceNet for feature extraction, and a Support Vector Machine (SVM) for identity classification. The system is implemented as a standalone application on an edge computing device (mini PC) integrated with an electronic door lock. All detection and decision-making processes are performed locally without reliance on cloud services. System evaluation was conducted under various testing scenarios, including variations in lighting intensity, camera distance, facial attributes, and unregistered face testing. Experimental results show that the system achieved an accuracy of 96.25%. A precision of 100% indicates that no unauthorized access was granted. The recall reached 92.50%, reflecting a small proportion of rejected authorized users. The F1-score of 96.11% demonstrates balanced performance. The False Acceptance Rate was 0%, confirming complete prevention of illegal access. The False Rejection Rate was 7.50%, which remains acceptable in high-risk security environments. The system consistently rejected all unregistered faces and operated in real time with an average door unlocking response time of approximately 1.3 seconds. In conclusion, the proposed system provides reliable recognition performance with a strong emphasis on preventing false acceptance. These findings indicate its suitability for enhancing security in high-risk weapon storage facilities.]]>
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