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arXiv · 2607.19722

ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment

Stefanos Gkikas·Yu Fang·Christian Arzate Cruz·Muhammad Umar Khan·Raul Fernandez Rojas
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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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Stefanos Gkikas, Yu Fang, Christian Arzate Cruz, Muhammad Umar Khan, Raul Fernandez Rojas. 2026-07-22. ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment. https://arxiv.org/abs/2607.19722

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