# STAR-FER: Adaptive Selective Restoration for Edge-Based Low-Resolution Facial Expression Recognition

**Authors:** ZhongYang Wu, ZhenFeng Gan, Di Zhao, XiaoJun Zhu, HouDe Liu  
**Lab:** Tsinghua Shenzhen International Graduate School · **Published:** 30 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.41310) · [PDF](https://arxiv.org/pdf/2610.41310) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.41310.html)

## Key numbers

- **80.92%**: STAR-FER achieves 80.92% mean accuracy and 71.64% Macro-F1
- **71.64%**: STAR-FER achieves 80.92% mean accuracy and 71.64% Macro-F1
- **54.29%**: It surpasses SR-FER by 1.04 accuracy points while requiring 8.84 GFLOPs, a 54.29% reduction

## Abstract

Variable interaction distances continuously degrade facial resolution in edge-based affective interaction, making low-resolution facial expression recognition (FER) difficult under limited computation. Full-face image or feature restoration incurs substantial overhead, whereas lightweight recognition pipelines may miss local cues that remain discriminative after degradation. We propose STAR-FER, a scale-and-task-adaptive selective restoration framework that balances recognition performance and resource constraints. A lightweight image-scale conditioned Gate jointly predicts a pixel-wise spatial ranking and a continuous restoration budget, then applies exact-support projection to obtain an input-dependent free-form mask. The mask-conditioned restorer retains complete facial context while concentrating sparse restoration and residual insertion on the selected support. Across seven degradation scales from x2 to x8 on the complete RAF-DB test set, STAR-FER achieves 80.92% mean accuracy and 71.64% Macro-F1, improving the low-resolution ResNet-18 baseline by 2.25 and 2.71 percentage points, respectively. It surpasses SR-FER by 1.04 accuracy points while requiring 8.84 GFLOPs, a 54.29% reduction, with 28.01% mean mask coverage, and maintains effective recognition at unseen non-integer scales. These results show that spatial-ranking and continuous-budget driven selective restoration reduces spatial modification and computation while maintaining low-resolution FER performance, offering an efficient path to resource-constrained affective interaction.

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