Real-time facial emotion recognition using deep learning has become an important research area in computer vision and affective computing. The objective of this study is to design and implement a robust system capable of detecting and classifying human emotions from live video streams with high accuracy and low latency. The proposed framework utilizes Convolutional Neural Networks (CNNs) to automatically extract discriminative facial features from input images and classify them into basic emotional categories such as happiness, sadness, anger, fear, surprise, disgust, and neutrality. The system consists of three main stages: face detection, pre-processing, and emotion classification. Face regions are detected using a deep learning-based detector, followed by normalization and resizing before being fed into the CNN model. To enhance performance, transfer learning techniques with pre-trained models are applied and fine-tuned on benchmark facial expression datasets. Experimental results demonstrate that the model achieves reliable accuracy under varying lighting conditions, head poses, and facial orientations while maintaining real-time processing speed. The proposed system has potential applications in healthcare, education, human-computer interaction, surveillance, and assistive technologies, providing an intelligent solution for emotion-aware systems. Nonverbal notes conveyed by facial expressions are extremely important for interpersonal relationships. A frequent part of human mechanical interfaces is the automatic facial expression detection. It can also be used in clinical practice and behavioural science. People actually recognize facial expressions, but machine-based detection of solid expression remains a challenge. Expressions can be seen as distortions in the face and changes in facial pigmentation or their spatial relationships or changes in facial pigmentation in terms of automatic detection.
Ms.N.Sowmiya et al. (Thu,) studied this question.