Human Emotion Detection Through Real-Time Facial Expressions Using Deep Learning

Facial emotion detection is one of the great steps in the world of artificial intelligence where machines are able to show human emotions with facial detection. The real time system for facial expression recognition using deep learning techniques has proven to have practical applications in healthcare, education and human computer interaction. It integrates two powerful pre trained networks, namely, EfficientNet B3 and ResNet 50 for accurate and efficient emotion classification. Training and evaluation is done on the RAF-DB dataset that has over 15,000 real world facial images and with 7 basic emotions assigned. The proposed system is one of the ones that include essential preprocessing techniques as face detection, alignment, normalization and data augmentation to improve performance. The training accuracy is 87.58 % and the testing accuracy is 82.53 %, meaning that it generalizes strongly. It is designed to be run in real time with input from webcam and OpenCV integration. This proves the suitability of developing intelligent systems that are capable of understanding and responding to human emotional states by means of deep learning.