Dynamic Resolution Guidance for Facial Expression Recognition

TL;DR

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.

Abstract

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.

Figures (7)

• Figure 1: This is a group photo featuring Chinese celebrities. Due to the shooting angle and distance, the resolution of each individual's face varies. We have selected facial images of three celebrities on the right for an intuitive visual comparison. The images demonstrate the differences in clarity at three distinct resolutions: high, medium, and low.
• Figure 2: This is pipeline of our proposed method.
• Figure 3: Our RRN is based on Deep residual network(ResNet18)
• Figure 4: BasicBlock
• Figure 5: Multi-Resolution Adaptation Facial Expression Recognition Network.