Leveraging Symmetry and Addressing Asymmetry Challenges for Improved Convolutional Neural Network-Based Facial Emotion Recognition

This study introduces a custom-designed CNN architecture that extracts robust, multi-level facial features and incorporates preprocessing techniques to correct or reduce asymmetry before classification. The innovative characteristics of this research lie in its integrated approach to overcoming facial asymmetry challenges and enhancing CNN-based emotion recognition. This is completed by well-known data augmentation strategies—using methods such as vertical flipping and shuffling—that generate symmetric variations in facial images, effectively balancing the dataset and improving recognition accuracy. Additionally, a Loss Weight parameter is used to fine-tune training, thereby optimizing performance across diverse and unbalanced emotion classes. Collectively, all these contribute to an efficient, real-time facial emotion recognition system that outperforms traditional CNN models and offers practical benefits for various applications while also addressing the inherent challenges of facial asymmetry in emotion detection. Our experimental results demonstrate superior performance compared to other CNN methods, marking a step forward in applications ranging from human–computer interaction to immersive technologies while also acknowledging privacy and ethical considerations.