VISUAL BASED AGE AND GENDER DETECTION USING DEEP LEARNING AND OPENCV

In the period of smart operations and mortal-computer commerce, the capability to detect age and gender using facial features has become largely applicable.This paper presents a real-time system for visual-based age and gender discovery using pre-trained deep literacy models integrated with OpenCV.The system detects mortal faces from images or webcam feeds and predicts the age range and gender using Caffe-grounded Convolutional Neural Networks (CNN).Enforced using Python and Beaker, the system delivers a responsive and cybersurfer accessible interface that streams annotated labors in real time.The proposed frame showcases the eventuality of combining deep literacy with featherlight, web-grounded deployment and emphasizes the part of AI in demographic analytics, surveillance, retail personalization, and stoner-apprehensive systems.