Dynamic Resolution Guidance for Facial Expression Recognition
Jie Ou Xu Li Tianxiang Jiang Yuanlun Xie
Facial expression recognition (FER) is vital for human-computer interaction
and emotion analysis, yet recognizing expressions in low-resolution images
remains challenging. This paper introduces a practical method called Dynamic
Resolution Guidance for Facial Expression Recognition (DRGFER) to effectively
recognize facial expressions in images with varying resolutions without
compromising FER model accuracy. Our framework comprises two main components:
the Resolution Recognition Network (RRN) and the Multi-Resolution Adaptation
Facial Expression Recognition Network (MRAFER). The RRN determines image
resolution, outputs a binary vector, and the MRAFER assigns images to suitable
facial expression recognition networks based on resolution. We evaluated DRGFER
on widely-used datasets RAFDB and FERPlus, demonstrating that our method
retains optimal model performance at each resolution and outperforms
alternative resolution approaches. The proposed framework exhibits robustness
against resolution variations and facial expressions, offering a promising
solution for real-world applications.