ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment

2026-07-23 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Medical Devices & Health Technology, Health & Medical Research · Depth: Expert, extended

Summary

ReFace, a novel video-based pipeline, enhances automatic pain assessment by reorganizing facial input into four spatial quadrants prior to tokenization, rather than processing the entire face as a single region. This method, evaluated on the AI4Pain dataset, achieved 56.00% accuracy on the test set using video-only data, marking the highest reported accuracy under the fixed AI4Pain benchmark protocol among compared methods. The four-quadrant configuration, despite using the same total pixel budget as full-face input, demonstrated superior performance. Notably, a single quadrant, utilizing only one-quarter of the pixels, maintained competitive accuracy at a significantly reduced computational cost, suggesting its potential for resource-constrained applications. The pipeline involves MTCNN face detection, resizing to 224x224 pixels, partitioning into 112x112 pixel quadrants, and a token mixing process that fuses spatiotemporal information using frame sampling, axis folding, channel concatenation, and Fourier features, all within an attention-based architecture.

Key takeaway

For Machine Learning Engineers developing facial analysis systems, especially for pain assessment, you should consider implementing spatial reorganization of facial input. ReFace demonstrates that partitioning faces into quadrants before tokenization significantly boosts accuracy to 56.00% on the AI4Pain dataset, even with the same pixel budget. Furthermore, leveraging single quadrants can achieve competitive performance at substantially lower computational cost, enabling deployment in resource-constrained environments.

Key insights

Spatial reorganization of facial input before tokenization improves pain assessment accuracy.

Principles

• Explicit spatial partitioning enhances feature learning.
• Reduced pixel input can maintain competitive accuracy.
• Optimal temporal sampling is crucial for multi-region fusion.

Method

Partition facial video into four quadrants, then fuse spatiotemporal information via frame sampling, axis folding, channel concatenation, and Fourier features before attention-based processing.

In practice

• Apply quadrant-based tokenization for facial analysis tasks.
• Consider single-quadrant models for low-resource deployments.
• Optimize temporal stride for specific multi-region configurations.

Topics

• Automatic Pain Assessment
• Facial Video Analysis
• Spatiotemporal Representation
• AI4Pain Dataset
• Quadrant Segmentation
• Transformer Architectures
• Computational Efficiency

Best for: Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.