Cross Age Face Recognition

Abstract

A robust face recognition system designed for real-time detection and identifica- tion of individuals in video streams. The system leverages DeepFace and FaceNet for high-accuracy facial feature extraction and matching. Using a reference image for each person, the system accurately identifies individuals by comparing the live video feed with a video that contains images of the person from younger to older age which is being generated. Hence this includes live camera detection, face aging simulation, and automated alerting through email notifications when a recognized individual is detected. Experimental results demonstrate the system’s ability to perform reliable face recognition under varying conditions. Addition- ally, the integration of an alert system enhances the practical applicability of the solution for finding missing childhren. This highlights the potential of deep learning models in enhancing the reliability and efficiency of automated recogni- tion systems while also exploring avenues for further optimization in real-world deployments.