Deep Learning Techniques for Real-Time Face Recognition and Surveillance Systems

Abstract

Real-time face recognition has become a central component of intelligent surveillance because it can convert continuous video into identity-aware events while reducing dependence on manual monitoring. This methodology paper develops a deep learning framework for real-time face detection, alignment, feature extraction, identity matching, and surveillance decision making using techniques available up to 2017. The proposed pipeline combines multi-task cascaded convolutional face detection with a compact deep face embedding network and similarity-based recognition. The methodology emphasizes frame-rate control, temporal confirmation, confidence thresholds, and database indexing so that high recognition accuracy is balanced against latency and false alarms. A literature-grounded evaluation compares influential systems including DeepID, DeepFace, VGGFace, FaceNet, DeepID2+, Center Loss, and SphereFace. Published LFW results show the rapid improvement of deep face representations from approximately 97% verification accuracy to above 99%, while MTCNN provides a practical real-time front end for detection and alignment. The paper concludes that an efficient surveillance system should separate detection, representation, and decision layers and should evaluate accuracy jointly with processing speed, false acceptance, false rejection, and robustness under pose, illumination, motion blur, and occlusion.