International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Emotion detection plays a vital role in enhancing human–computer interaction by enabling systems to recognize and respond to human emotions. This project focuses on the development of an automated emotion detection system using Convolutional Neural Networks (CNNs), a powerful deep learning architecture for image-based pattern recognition. The system is trained on facial expression datasets such as FER-2013 or CK+ that contain labeled images representing emotions like happy, sad, angry, fear, surprise, disgust, and neutral. The images are preprocessed through grayscale conversion, normalization, and data augmentation to improve model performance and generalization. A CNN model is designed and trained to extract hierarchical features from facial images, followed by fully connected layers for emotion classification. The model’s performance is evaluated using metrics such as accuracy, precision, recall, and confusion matrix analysis. Once trained, the model is deployed using a web interface or real-time video stream to detect emotions from live webcam input.
The results demonstrate that the CNN-based system achieves high accuracy in identifying human emotions and performs effectively in real-world scenarios. This project highlights the capability of deep learning techniques to build robust emotion recognition systems applicable in domains such as human–computer interaction, mental health monitoring, and smart surveillance.

Downloads: | DOI: 10.17148/IJARCCE.2025.141038