Predicting Human Disagreement for Calibrated Dynamic Facial Expression Recognition

• URL: http://arxiv.org/abs/2609.17130v1
• Date: Tue, 15 Sep 2026 12:55:22 GMT
• Title: Predicting Human Disagreement for Calibrated Dynamic Facial Expression Recognition
• Abstract summary: We propose a disagreement-aware DFER framework that trains directly on the raw annotator count vector.<n>A separate ambiguity head predicts annotation entropy for unseen clips, and a monotone Chow-style reject rule combines predicted ambiguity, vacuity, temporal instability, and input quality.<n>On DFEW, the method preserves recognition accuracy while reducing ECE by 30% and AURC by 15%, and predicted ambiguity reaches a Spearman correlation of 0.52 with the annotation entropy of test clips.
• Score: 12.523217686212197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic facial expression recognition (DFER) benchmarks such as DFEW provide multiple annotator votes per clip, yet most models collapse them to a majority label and cannot represent human disagreement at inference time. We propose a disagreement-aware DFER framework that trains directly on the raw annotator count vector using a Dirichlet-Multinomial likelihood. Unlike mean-only soft-label objectives, the proposed likelihood provides scale-sensitive supervision for the Dirichlet concentration while preserving the predictive mean. A separate ambiguity head predicts annotation entropy for unseen clips, and a monotone Chow-style reject rule combines predicted ambiguity, vacuity, temporal instability, and input quality for selective prediction. On DFEW, the method preserves recognition accuracy while reducing ECE by 30% and AURC by 15%, and predicted ambiguity reaches a Spearman correlation of 0.52 with the annotation entropy of test clips. The calibration and selective-prediction gains transfer to FERV39k and remain under identity- and movie-disjoint DFEW splits.

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