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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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Related Experiment Video

Updated: Jan 17, 2026

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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An Enhanced Adaptive Confidence Margin for Semi-Supervised Facial Expression Recognition.

Hangyu Li, Nannan Wang, Xi Yang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 22, 2025
    PubMed
    Summary

    This study introduces an Enhanced Adaptive Confidence Margin (EACM) for semi-supervised learning (SSL) in facial expression recognition (FER). EACM improves how unlabeled facial data is used, outperforming traditional methods.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Facial Expression Recognition (FER) often requires extensive labeled data, which is costly to obtain.
    • Semi-supervised learning (SSL) offers a solution by utilizing abundant unlabeled data.
    • Existing SSL methods for FER have limitations in handling varying category confidence and efficient unlabeled data utilization.

    Purpose of the Study:

    • To propose an Enhanced Adaptive Confidence Margin (EACM) for improved SSL in FER.
    • To address the limitations of fixed-threshold SSL methods in FER.
    • To enhance the efficient utilization of unlabeled facial expression samples.

    Main Methods:

    • Developed EACM with dynamic thresholds tailored to different facial expression categories.
    • Partitioned unlabeled data into subsets based on confidence margins for differential treatment.
    • Implemented pseudo-labeling for high-confidence samples and a feature-level contrastive objective for low-confidence samples.

    Main Results:

    • EACM demonstrated superior performance on both image-based and video-based FER datasets.
    • The proposed method significantly surpassed fully-supervised baselines in a semi-supervised setting.
    • EACM showed potential for leveraging cross-dataset unlabeled samples to boost performance.

    Conclusions:

    • EACM effectively addresses the challenges of category-specific confidence and efficient unlabeled data usage in SSL for FER.
    • The method offers a practical and effective approach to enhance FER systems with limited labeled data.
    • EACM presents a promising direction for improving FER model generalization and performance through cross-dataset learning.