ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment
Stefanos Gkikas, Yu Fang, Christian Arzate Cruz, Muhammad Umar Khan, Raul Fernandez Rojas
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.
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Classified with taxonomy v2 on Wed, 2 Sept 2026.