Real-Time Emotion Recognition from Facial Expressions Using Deep CNNs

Author(s)	Ms. Manjula K, Dr. Yuvaraja A, Dr. Gayathri A C, Dr. Pooja P Yalavathi, Dr. Punya Patil G M
Country	India
Abstract	Emotion recognition from facial expressions has emerged as a powerful tool in fields ranging from mental health assessment to intelligent tutoring systems and targeted marketing. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have significantly improved the accuracy and speed of emotion detection in real-time scenarios. This paper explores the development and implementation of a real-time facial motion recognition system using deep neural networks. The system captures facial images, preprocesses them for landmark detection, and classifies emotions such as happiness, sadness, anger, surprise, and neutral expressions. The proposed approach leverages modern architectures trained on large-scale datasets, such as FER2013, to achieve high recognition rates. This study highlights the potential of AI-powered emotion detection in creating adaptive, human-centric applications.
Keywords	Emotion Recognition, Facial Expressions, Deep Learning, Convolutional Neural Networks (CNNs), Real-time Detection, FER2013 Dataset, Landmark Detection, Human-Computer Interaction, Mental Health Monitoring, Intelligent Tutoring Systems, Computer Vision, Adaptive Applications
Field	Computer Applications
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-10-18

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