ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition

Autor:	Roy, Arnab Kumar, Kathania, Hemant Kumar, Sharma, Adhitiya, Dey, Abhishek, Ansari, Md. Sarfaraj Alam
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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 three open-source databases: FER2013, RAF-DB, and AffectNet, achieving accuracies of 79.79%, 94.76%, and 72.39%, respectively. The proposed network outperforms state-of-the-art models across all three databases. The source code for ResEmoteNet is available at https://github.com/ArnabKumarRoy02/ResEmoteNet. Comment: 5 pages, 3 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.10545 Zobrazit plný text záznamu View this record from Arxiv