Revisiting the Volumetric Data of 4DME: Compression, Extension and Benchmarking for Micro-Expression Analysis

Micro-expressions are brief, involuntary facial movementsthat reveal subtle affective states. While most computational work fo-cuses on micro-expression recognition, which classifies clips into dis-crete emotion categories, Micro-Expression Action Unit (ME-AU) de-tection offers a more theory-consistent formulation grounded in the Fa-cial Action Coding System. Despite its finer granularity, ME-AU de-tection remains comparatively under-explored due to the limited avail-ability of high-quality data for efficient automatic models. Recent volu-metric micro-expression datasets provide temporally coherent 3D facialrecordings with reliable AU labels, creating new opportunities for fine-grained analysis. However, their adoption remains limited due to largedata volumes, heterogeneous formats, and the absence of standardizedbenchmarks, which together hinder reproducibility and cross-study com-parison. Moreover, because of the small spatio-temporal spanning of theAUs, designing models that effectively combine volumetric representa-tions with temporal dynamics remains a non-trivial research challenge.To address these barriers, we introduce an extended and standardizedrelease of the 4DME dataset with cleaned data and normalized format-ting, accompanied by a compressed version to reduce storage overhead.We further design VoluME, a family of dual-stream optical-flow-basedarchitecture for volumetric ME-AU detection. To determine appropriatesettings for compression, we combine state-of-the-art compression base-lines, perceptual evaluation, and downstream task-specific benchmark-ing. As the first work dealing with volumetric ME-AU detection undercompression, our results show that a compression ratio of approximately3000× can be achieved with ≈ 1% F1 degradation. The extended releaseadditionally introduces explicit two-way cultural balancing, supportingfuture cross-cultural micro-expression studies.