ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment

2026-07-21

AI Summary

This study addresses the challenge of automatic pain assessment arising from the spatial heterogeneity of facial pain cues. To this end, the authors propose ReFace, a novel approach that partitions facial videos into four spatial quadrants and performs tokenization and spatiotemporal modeling on each quadrant independently, rather than processing the entire face as a whole. This design enables more precise capture of localized pain-related features. ReFace further introduces an innovative spatial recombination strategy that enhances model performance without increasing the total number of pixels. Notably, the method demonstrates that using only a single quadrant can maintain competitive accuracy while substantially reducing computational cost. Evaluated on the AI4Pain dataset under the standard benchmark protocol, ReFace achieves a test accuracy of 56.00% using video input alone, setting a new state-of-the-art result.

Abstract

Automatic pain assessment from facial video remains challenging due to the spatial heterogeneity of pain-related facial cues. This study proposes ReFace, a spatial reorganization pipeline that divides facial input into four spatial quadrants before tokenization, rather than processing the entire face as a single region. Evaluated on the AI4Pain dataset, the proposed approach achieves $56.00\%$ accuracy on the test set using video only, achieving the highest reported accuracy under the fixed AI4Pain benchmark protocol among the compared methods. Notably, the four-quadrant configuration processes the same total pixel budget as the full-face input, yet achieves higher accuracy, suggesting that spatial reorganization can improve performance under the proposed tokenization design. A single quadrant region, processing just one quarter of those pixels, remains competitive at a fraction of the computational cost.

Problem

• pain assessment
• facial video
• spatial heterogeneity
• automatic pain recognition
• facial cues

Innovation

• spatial reorganization
• facial pain assessment
• video-based pain recognition
• quadrant tokenization
• computational efficiency