With the transition of facial expression recognition (FER) from laboratory-controlled to
challenging in-the-wild conditions and the recent success of deep learning techniques in …

Emotion recognition has attracted major attention in numerous fields because of its relevant
applications in the contemporary world: marketing, psychology, surveillance, and …

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 received increasing interest in computer vision. We
propose the TransFER model which can learn rich relation-aware local representations. It …

Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the
uncertainties caused by ambiguous facial expressions, low-quality facial images, and the …

In facial expression recognition (FER), the uncertainties introduced by inherent noises like
ambiguous facial expressions and inconsistent labels raise concerns about the credibility of …

Due to the subjective annotation and the inherent inter-class similarity of facial expressions,
one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity …

Occlusion and pose variations, which can change facial appearance significantly, are two
major obstacles for automatic Facial Expression Recognition (FER). Though automatic FER …

Facial Expression Recognition (FER) in the wild is extremely challenging due to occlusions,
variant head poses, face deformation and motion blur under unconstrained conditions …

Facial expression recognition has been an active area of research over the past few
decades, and it is still challenging due to the high intra-class variation. Traditional …