Deep Learning Meets Emotion: CNN and Attention Mechanisms for Real-Time Emotion Detection

Facial emotion recognition plays a significant role in applications such as human-computer interaction, mental health monitoring, and security systems. Traditional methods, including Local Binary Patterns (LBP) and Support Vector Machines (SVM), often struggle with variations in lighting, pose, and subtle emotional expressions due to their reliance on handcrafted features. To address these challenges, this study presents a Convolutional Neural Network (CNN) integrated with an attention mechanism to enhance emotion classification accuracy. The model utilizes CNNs for automatic feature extraction while employing the attention mechanism to prioritize critical facial regions, thereby improving classification performance. The system is evaluated using the FER-2013 dataset, achieving an accuracy of 75%, outperforming conventional approaches. The results demonstrate the model’s robustness in handling diverse conditions, making it a promising solution for real-world applications. Identified challenges include computational efficiency and real-time adaptability, which can be addressed in future work by optimizing hyperparameters and integrating multimodal data.