Papers › Facial expression recognition with grid-wise attention and visual transformer
Facial expression recognition with grid-wise attention and visual transformer
Qionghao Huang, Changqin Huang, Xizhe Wang, Fan Jiang
Facial Expression Recognition (FER) has achieved remarkable progress as a result of using Convolutional Neural Networks (CNN). Relying on the spatial locality, convolutional filters in CNN, however, fail to learn long-range inductive biases between different facial regions in most neural layers. As such, the performance of a CNN-based model for FER is still limited. To address this problem, this paper introduces a novel FER framework with two attention mechanisms for CNN-based models, and these two attention mechanisms are used for the low-level feature learning the high-level semantic representation, respectively. In particular, in the low-level feature learning, a grid-wise attention mechanism is proposed to capture the dependencies of different regions from a facial expression image such that the parameter update of convolutional filters in low-level feature learning is regularized. In the high-level semantic representation, a visual transformer attention mechanism uses a sequence of visual semantic tokens (generated from pyramid features of high convolutional layer blocks) to learn the global representation. Extensive experiments have been conducted on three public facial expression datasets, CK+, FER+, and RAF-DB. The results show that our FER-VT has achieved state-of-the-art performance on these datasets, especially with a 100% accuracy on CK + datasets without any extra training data.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Facial Expression Recognition (FER) | FER+ | FER-VT | Accuracy | 90.04 | #8 of 14 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections