An essential skill for effective communication is the ability to express specific sentiment and emotion in a conversation. Any robust dialogue system should handle the combined effect of both sentiment and emotion while generating responses. This is ex…
From the perspective of social science, understanding group emotion has become increasingly important for teams to considerably accomplish organizational work. Currently, automatically analyzing the perceived affect of a group of people has been receiv…
Antagonistic crowd behaviors are often observed in cases of serious conflict. Antagonistic emotions, which is the typical psychological state of agents in different roles (i.e., cops, activists, and civilians) in crowd violence scenes, and the way they…
With the advent of consumer grade virtual reality (VR) headsets and physiological measurement devices, new possibilities for mediated social interaction emerge enabling the immersion to environments where the visual features react to the users’ …
The advancement of Human-Robot Interaction (HRI) drives research into the development of advanced emotion identification architectures that fathom audio-visual (A-V) modalities of human emotion. State-of-the-art methods in multi-modal emotion recogniti…
Humans perceive emotion from each other using a holistic perspective, accounting for diverse personal, non-emotional variables, such as age and personality, that shape expression. In contrast, today’s algorithms are mainly designed to recognize …
Advertisements (ads) often contain strong emotions to capture audience attention and convey an effective message. Still, little work has focused on affect recognition (AR) from ads employing audiovisual or user cues. This work (1) compiles an affective…
Pain inadequate treatment is frequent in modern society, with major medical, ethical, and financial implications. In many healthcare environments, pain is quantified prevalently through subjective measures, such as self-reports from patients or health …
The two-stage fuzzy fusion based-convolution neural network is proposed for dynamic emotion recognition by using both facial expression and speech modalities, which not only can extract discriminative emotion features which contain spatio-temporal info…
Facial action unit (AU) detection in the wild is a challenging problem, due to the unconstrained variability in facial appearances and the lack of accurate annotations. Most existing methods depend on either impractical labor-intensive labeling or inac…