mlsanityData to 14 Oct 2026

Enhancing Ambiguous Dynamic Facial Expression Recognition with Soft Label-based Data Augmentation

arXiv 2506.20867 ↗First posted 25 Jun 2025cs.CVcited by 1

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Abstract (arXiv)

Dynamic facial expression recognition (DFER) is a task that estimates emotions from facial expression video sequences. For practical applications, accurately recognizing ambiguous facial expressions -- frequently encountered in in-the-wild data -- is essential. In this study, we propose MIDAS, a data augmentation method designed to enhance DFER performance for ambiguous facial expression data using soft labels representing probabilities of multiple emotion classes. MIDAS augments training data by convexly combining pairs of video frames and their corresponding emotion class labels. This approach extends mixup to soft-labeled video data, offering a simple yet highly effective method for handling ambiguity in DFER. To evaluate MIDAS, we conducted experiments on both the DFEW dataset and FERV39k-Plus, a newly constructed dataset that assigns soft labels to an existing DFER dataset. The results demonstrate that models trained with MIDAS-augmented data achieve superior performance compared to the state-of-the-art method trained on the original dataset.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 51 of its 79 references that are in the map. First posted on arXiv 25 Jun 2025.

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51 of 79 references.

References · 51

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38 more
  1. 2020DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wildcited by 128
  2. 2021Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversitycited by 128
  3. 2021AugMax: Adversarial Composition of Random Augmentations for Robust Trainingcited by 90
  4. 2021Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognitioncited by 89
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  38. 2025MIDAS: Mixing Ambiguous Data with Soft Labels for Dynamic Facial Expression Recognitioncited by 4

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  1. 2026Revisiting Emotions Representation for Recognition in the Wildcited by 0