This Facial Emotion Recognition (FER) is a pivotal element of Human & Computer Interaction. In This paper we present the creation of a instantaneous system that can identify and categorize human expressions into seven basic emotions: 1. anger, 2. disgust, 3. fear, 4. happiness, 5. sadness, 6. surprise, and 7. neutral. By leveraging Deep Learning (DL) architectures & specifically Convolutional Neural Networks (CNNs), we achieve high accuracy in varying environmental conditions. The system works by capturing live video from a webcam, detecting faces in each frame, and then using a trained model to recognize and predict emotions instantly in real time.
Keywords
Artificial Intelligence (AI), Facial Emotion Recognition, Deep Learning, CNN.
Conclusion
In this paper, a live human emotion detection system on artificial intelligence & deep learning has been presented. The project successfully developed an accurate and efficient system for detecting and classifying emotions through facial expressions [18]. The system utilizes computer vision techniques & a Convolutional Neural Network (CNN) to accurately recognize & classify human emotions.
The proposed approach is capable of processing live video I/P & providing live emotion predictions. The results demonstrate that the system performs efficiently under different conditions & is used in human-computer interaction, mental health monitoring, & smart user interfaces. Deep convolutional neural networks have achieved robust performance in facial expression recognition across different environmental conditions [19].
Future improvements may include increasing model accuracy, supporting more emotion categories, & enhancing performance under complex real-world conditions. Future research directions include multimodal information fusion, personalized emotion recognition, and interdisciplinary cooperation [20].
References
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How to Cite This Paper
Prachi Urgunde, Deepali Gille, Gauri Hushangabadkar, Neha Bokad, Mohini Shrikhande,
Prof. Pravin Kaware (2026). Real-Time Human Facial Emotion Recognition System Using Deep Learning. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
