Real-Time Facial Recognition in Computer Vision for Industrial Security

In industrial security, facial recognition is crucial because it allows for precise and timely identification of people, which is necessary to protect assets and guarantee worker safety. This research presents a unique solution to the problems of real-time face recognition in industrial settings by combining state-of-the-art technology. “Deep Face Recognition with FaceNet,” “Real-Time Face Detection with Faster R-CNN,” and “Privacy-Preserving Techniques with Homomorphic Encryption” are all integrated into the suggested way to produce a secure and private system. Through a series of tests, we show in our studies how successful this strategy is. Firstly, Faster R-CNN achieves high accuracy face detection with great efficiency in real-time video streams. FaceNet then does a great job of identifying these faces and associating them with recognized people, providing accurate identification. Our method relies heavily on privacy preservation, which is accomplished by using homomorphic encryption. This guarantees the confidentiality of face data even as it is being recognized. Our results show that the suggested approach performs better than conventional approaches in a number of areas. It uses deep learning to adjust to changing situations and provides real-time accuracy for quick recognition. Moreover, it is unique due to its use of homomorphic encryption, which protects users' privacy while identifying them. To sum up, our method is a major step forward in real-time face recognition for industrial security. Enhancing accuracy, speed, and privacy, it meets the complex requirements of contemporary security systems. Future use of this technique should improve industrial processes' efficiency and safety.