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Abstract
Facial emotion recognition has become an essential component of intelligent systems aiming to bridge the gap between human emotions and machine understanding. This project, “Real-Time Facial Emotion Detection Using Deep Learning and AI,” presents an efficient approach to identifying and classifying human emotions from facial expressions in real time. The proposed system utilizes deep learning techniques, particularly Convolutional Neural Networks (CNNs), to automatically extract discriminative features from facial images and classify them into basic emotion categories such as happiness, sadness, anger, fear, surprise, disgust, and neutrality. The model is trained on publicly available facial emotion datasets to ensure high accuracy and robustness across diverse facial features, lighting conditions, and orientations. Using real-time video input from a webcam or camera feed, the system performs face detection, preprocessing, and emotion classification with minimal latency. The integration of AI-based deep learning models enables adaptive learning, improved generalization, and enhanced performance compared to traditional machine learning methods. Experimental results demonstrate that the proposed system achieves high accuracy and responsiveness, making it suitable for real-world applications such as human–computer interaction, virtual assistants, mental health assessment, and smart surveillance. The project showcases how AI and deep learning can be effectively combined to build emotionally intelligent systems capable of understanding and responding to human affective states in real time.
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