International Research Journal of Education and Technology

Emotion recognition through facial expressions is a critical area of computer vision, enabling systems to understand human emotions for applications such as human-computer interaction, healthcare, and security. Recent advancements have led to the development of various deep learning models for emotion recognition. While transformers have shown promise, they often come with high computational costs, particularly in handling space-time attention mechanisms. To address this, we propose a novel approach for emotion recognition using a Convolutional Neural Network (CNN), which effectively extracts spatial features from facial images while being computationally efficient. Our CNN-based model is designed to focus on learning discriminative facial features that are crucial for recognizing a wide range of emotions. The model leverages a frame-wise deep learning architecture, allowing it to process each frame independently while capturing important facial patterns. We evaluate the performance of the proposed CNN-based model on benchmark dataset, Fer-2013plus (Facial-Emotion-Recognition), with geometric transformations used for data augmentation to address class imbalances. The results demonstrate that our CNN-based approach achieves competitive performance, either outperforming or matching the accuracy of techniques in emotion recognition. Furthermore, an ablation study on the challenging Fer2013+ dataset highlights the potential and effectiveness of the proposed model for handling complex emotion recognition tasks in real-world applications.