Live facial emotion detection and age-gender prediction using OpenCV and deep learning

Facial emotion recognition (FER) is crucial in education, healthcare, marketing, and human-computer interaction. This paper reviews live FER systems employing Convolutional Neural Networks (CNN) and OpenCV, integrating age and gender detection to improve accuracy and adaptability. While traditional FER methods focus on static images, human emotions are better analyzed through continuous video frames. By combining CNNs with long short-term memory (LSTM) networks, the approach captures both spatial and temporal features, enhancing real-time emotion classification.