Dynamic expression recognition in the wild is a challenging task due to various obstacles,
including low light condition, non-positive face, and face occlusion. Purely vision-based …

Though multimodal emotion recognition has achieved significant progress over recent years
the potential of rich synergic relationships across the modalities is not fully exploited. In this …

Automated emotion recognition (AER) has a growing number of applications, ranging from
behavior analysis in assistive robotics and smart e-learning to depression or pain detection …

Deep learning techniques have drawn considerable interest in emotion recognition due to
recent technological developments in healthcare analytics. Automatic patient emotion …

Affective Behavior Analysis aims to facilitate technology emotionally smart, creating a world
where devices can understand and react to our emotions as humans do. To …

Abstract Affective Behavior Analysis aims to facilitate technology emotionally smart creating
a world where devices can understand and react to our emotions as humans do. To …

In video-based emotion recognition (ER), it is important to effectively leverage the
complementary relationship among audio (A) and visual (V) modalities, while retaining the …

Deep learning models for multimodal expression recognition have reached remarkable
performance in controlled laboratory environments because of their ability to learn …

Person or identity verification has been recently gaining a lot of attention using audio-visual
fusion as faces and voices share close associations with each other. Conventional …

Multimodal emotion recognition has immense potential for the comprehensive assessment
of human emotions, utilizing multiple modalities that often exhibit complementary …