Abstract Noisy label Facial Expression Recognition (FER) is more challenging than
traditional noisy label classification tasks due to the inter-class similarity and the annotation …

Facial expression recognition (FER) has attracted intensive attention due to its critical role in
various computer vision tasks. However, existing FER approaches suffer from either noisy …

Automatic depression analysis has been widely investigated on face videos that have been
carefully collected and annotated in lab conditions. However, videos collected under real …

The real-world facial expression recognition (FER) datasets suffer from noisy annotations
due to crowd-sourcing, ambiguity in expressions, the subjectivity of annotators, and inter …

The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed
proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel …

Although existing facial expression recognition (FER) methods have achieved great
success, their performance degrades significantly under noisy labels caused by low-quality …

Due to labeling subjectivity, label noises have become a critical issue that is addressed in
facial expression recognition. From the view of human visual perception, the facial exhibited …

Deep metric-based center loss has been widely used to enhance inter-class separability
and intra-class compactness of network features and achieved promising results in facial …

The shortage of emotion-annotated video datasets suitable for training and validating
machine learning models for facial expression-based emotion recognition stems primarily …

Large-scale facial expression datasets are primarily composed of real-world facial
expressions. Expression occlusion and large-angle faces are two important problems …