SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global