CP-Xception: A Lightweight Facial Expression Recognition Model For AI Companions

In this paper, we propose a lightweight facial expression recognition model: CP-Xception for AI companions, which is based on the Mini-Xception and features a small number of parameters, fast inference speed, and high recognition accuracy. Specifically, we integrate the feature segmentation concept from CSPNet into the model, splitting the input features into primary and secondary paths to extract deep and shallow features, respectively. We also incorporate the ParC module into the last two feature extraction stages of the backbone network. This enhancement enables the model to effectively capture both local details and global contextual information. The CP-Xception model is trained and evaluated on four public datasets: FER2013, FER2013Plus, CK_, and JAFFE. The results show that CP-Xception model achieves recognition accuracy improvements of 2.16 , 3.37 , and 4.31 over the Mini-Xception model on the FER2013, CK_, and JAFFE datasets respectively. And CP-Xception has only 30,149 parameters and 3.527 MFLOPs, which are approximately 50 of those of the Mini-Xception model， which makes the model more lightweight while also ensuring fast inference speed. We have deployed the model to the companion terminal for practical testing and observed satisfactory performance.