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PEGCL: Pseudo-Entropy Guided Complementary Learning for Robust Facial Expression Recognition Under Label Noise
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DOI：10.1109/tmm.2026.3672285.png)
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Abstract
En 中文
Facial Expression Recognition (FER) has recently plays a crucial role in advancing human-computer interaction systems, aiming to understand users' inner states and underlying intentions. However, FER in real-world scenarios remains challenging due to significant label noise, caused by ambiguous facial expressions in low-quality images and annotation bias. To tackle this issue, this paper proposes a novel framework, Pseudo-Entropy Guided Complementary Learning (PEGCL), designed to robustly handle noisy labels by leveraging complementary information, which trains networks using all complementary labels defined as “facial expression images that do not belong to complementary emotion labels.” This approach effectively utilizes non-target emotion labels to mitigate the impact of label noise, rather than relying solely on annotated emotion labels. Specifically, the proposed PEGCL framework consists of three components: logit normalization to stabilize predicted probabilities and prevent gradient explosions, transformed complementary learning to redistribute the optimization focus across complementary categories by leveraging pseudo-entropy guided, and random complementary label dropping to dynamically exclude subsets of complementary labels, enhancing generalization and preventing overfitting. These components collectively ensure robust and efficient optimization under noisy label conditions. Importantly, the proposed PEGCL does not require explicit noise estimation or complex label correction mechanisms, making it a simple and effective solution for real-world FER tasks. Extensive experiments on benchmark FER datasets demonstrate that PEGCL consistently outperforms existing methods, achieving the state-of-the-art robustness against label noise while maintaining high classification accuracy.
Keywords:
Facial expression recognition
label noise
complementary learning
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