Orthogonal Ensembles and Tested Explanations for Performer-Independent Body-Motion Emotion Recognition

2026-09-02

AI Summary

研究通过结合具有正交误差模式的模型，提高了基于身体动作的情绪识别准确率，并使用部分遮罩和反事实编辑方法验证了决策依据。

Abstract

We study body-only, 12-class acted-emotion classification from skeleton motion under leave-performer-out (LPO) evaluation, a hard, underdetermined setting: chance is 8.3%, and a protocol-matched reproduced STGCN++ baseline reaches only 25.73 +/- 4.03% Macro-F1. We show that reliable gains come not from a new architecture but from combining eleven models with orthogonal error modes: under 10-fold LPO cross-validation on the labeled training performers, an equal-weight logit-mean ensemble reaches 36.80 +/- 4.00% per-fold Macro-F1, a protocol-matched +11.07 pp (+43% relative) over the same-split reproduced baseline. Our central contribution is a tested explanation suite: for a strong ensemble member, part-masking and counterfactual edits show (rather than assert) that its decisions depend on motion-grounded body-region evidence, and this region saliency aligns with rule-based Laban Movement Analysis (LMA) attributes far more than with classical kinematics: region-level saliency-LMA Spearman rho = +0.500 versus +0.033, roughly 15x, and the alignment holds for the submitted 11-way ensemble itself at rho = +0.517; the audit is post hoc and needs no retraining. The same suite faithfully reports a negative: within-window temporal saliency is diffuse rather than localized.

Problem

• body-motion emotion recognition
• leave-performer-out evaluation
• skeleton motion
• acted-emotion classification
• orthogonal ensembles

Innovation

• Orthogonal Ensembles
• Body-Motion Emotion Recognition
• Saliency-LMA Alignment
• Part-masking
• Counterfactual Edits