In this study, we present a facial recognition system that integrates blurred face recognition with existing surveillance systems, enhancing security without the need for upgrading older cameras. The system automatically records key data such as timestamps of suspect appearances, significantly reducing the time required to review footage and track down suspects. The contributions of this research are as follows. (1) The system provides enhanced security by utilizing blurred face recognition without requiring costly hardware upgrades. (2) It enables the automated tracking and time-recording of suspects, reducing manual effort in surveillance review. (3) The system’s recognition model maintains high accuracy despite challenges in image quality and environmental factors. (4) The study highlights potential privacy concerns and legal considerations related to facial data storage, encouraging careful regulatory compliance. Experimental results show that the system maintains a high recognition rate within a 5.5 m range, with up to 100% accuracy under ideal conditions. However, its performance declines when the distance increases beyond 6 m, and it struggles with mask-wearing and extreme head angles. Future improvements to the face detection algorithm will increase detection rates and further enhance the system’s efficiency. By incorporating better visualization tools for suspect identification, the system can enable faster and more accurate crime investigations.