ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition
This work addresses the problem of improving accuracy and reducing loss in facial emotion recognition for computer vision applications, representing an incremental advancement.
The authors tackled facial emotion recognition by proposing ResEmoteNet, a deep learning architecture combining Convolutional, Squeeze-Excitation, and Residual Networks, which achieved accuracies of 79.79% on FER2013, 94.76% on RAF-DB, 72.39% on AffectNet-7, and 75.67% on ExpW, outperforming state-of-the-art models across all four databases.
The human face is a silent communicator, expressing emotions and thoughts through its facial expressions. With the advancements in computer vision in recent years, facial emotion recognition technology has made significant strides, enabling machines to decode the intricacies of facial cues. In this work, we propose ResEmoteNet, a novel deep learning architecture for facial emotion recognition designed with the combination of Convolutional, Squeeze-Excitation (SE) and Residual Networks. The inclusion of SE block selectively focuses on the important features of the human face, enhances the feature representation and suppresses the less relevant ones. This helps in reducing the loss and enhancing the overall model performance. We also integrate the SE block with three residual blocks that help in learning more complex representation of the data through deeper layers. We evaluated ResEmoteNet on four open-source databases: FER2013, RAF-DB, AffectNet-7 and ExpW, achieving accuracies of 79.79%, 94.76%, 72.39% and 75.67% respectively. The proposed network outperforms state-of-the-art models across all four databases. The source code for ResEmoteNet is available at https://github.com/ArnabKumarRoy02/ResEmoteNet.