Application of facial recognition technologies for enhancing control in information security systems

This article investigates contemporary advancements in information security technologies, with a focus on automated access control systems and their integration with biometric solutions. Particular emphasis is placed on the potential of facial recognition technologies to strengthen security protocols and streamline access management for restricted areas. A Python-based implementation utilizing the OpenCV library is presented, demonstrating real-time recognition capabilities and dynamic visitor data handling. In contrast to earlier conceptual works, this study provides a detailed description of the applied recognition algorithm, training procedure, and evaluation methodology. The system was tested in 200 experimental trials with 20 participants under varying conditions, including changes in lighting, distance, and partial occlusions such as masks and sunglasses. Performance metrics – accuracy, precision, recall, and F1-score – were calculated based on confusion-matrix analysis. The results confirm that the proposed prototype ensures reliable operation in diverse environments, offering a scalable and cost-effective solution for enhancing access control mechanisms. By combining technical rigor with practical implementation, the study underscores the feasibility of adopting facial recognition systems to improve both security and operational efficiency.