Facial Recognition and CCTV Integration for Enhanced Security Using Deep Learning Techniques

The integration of artificial intelligence has resulted in significant progress in the realm of surveillance and security systems. This work delves into the potential of Multitask Cascaded Convolutional Networks (MTCNN), a face localization framework, coupled with FaceNet, a face recognition algorithm. The process involves real-time conversion of CCTV footage into frames, followed by the application of MTCNN to localize faces within these frames. Face coordinates are then extracted and transformed into embeddings using FaceNet. Subsequently, the K-Nearest Neighbors (K-NN) classifier, trained with a local database, categorizes these embeddings. This framework achieves precise detection and localization of individuals, indicating their presence within images through bounding boxes. This integrated framework is capable of detecting, tracking, and recognizing individuals across diverse lighting conditions and crowded environments. Additionally, the system contributes to criminal tracking and can help in lookout and missing person cases. The system also includes a user-friendly Graphical User Interface (GUI) for live monitoring of camera feeds and video footage. Moreover, the system presents the advantage of minimizing human resource requirements, thereby augmenting the operational efficiency of the surveillance system.