Facial Action Unit (AU) detection has gained significant research attention as AUs contain
complex expression information. In this paper, we unpack two fundamental factors in AU …

Facial expression recognition (FER) remains a challenging task due to the ambiguity and
subtlety of expressions. To address this challenge, current FER methods predominantly …

Abstract Facial Expression Analysis remains a challenging task due to unexpected task-
irrelevant noise, such as identity, head pose, and background. To address this issue, this …

Facial expression recognition (FER) is a critical task in multimedia with significant
implications across various domains. However, analyzing the causes of facial expressions is …

Label noise is inevitable in facial expression recognition (FER) datasets, especially for
datasets that collected by web crawling, crowd sourcing in in-the-wild scenarios, which …

Real-world Facial Expression Recognition (FER) suffers from noisy labels due to ambiguous
expressions and subjective annotation. Overall, addressing noisy label FER involves two …

Facial expression recognition (FER) aims to analyze emotional states from static images and
dynamic sequences, which is pivotal in enhancing anthropomorphic communication among …

Facial expression recognition (FER) plays a crucial role in various applications, including
human–computer interaction and affective computing. However, the joint training of an FER …

Label biases in facial expression recognition (FER) datasets, caused by annotators'
subjectivity, pose challenges in improving the performance of target datasets when auxiliary …

On facial expression datasets with complex and numerous feature types, where the
significance and dominance of labeled features are difficult to predict, facial expression …