Affect recognition based on subjects' facial expressions has been a topic of major research
in the attempt to generate machines that can understand the way subjects feel, act and react …

This article presents a novel CNN-RNN based approach, which exploits multiple CNN
features for dimensional emotion recognition in-the-wild, utilizing the One-Minute Gradual …

With the advancement of multimedia and human-computer interaction, it has become
increasingly crucial to perceive people's emotional states in dynamic data (eg, video, audio …

As an emerging research topic for proximity service (ProSe), automatic emotion recognition
enables the machines to understand the emotional changes of human beings which can not …

Current state-of-the-art models for automatic facial expression recognition (FER) are based
on very deep neural networks that are effective but rather expensive to train. Given the …

Spatial-temporal feature learning is of vital importance for video emotion recognition.
Previous deep network structures often focused on macro-motion which extends over long …

How can we reliably transfer affect models trained in controlled laboratory conditions (in-
vitro) to uncontrolled real-world settings (in-vivo)? The information gap between in-vitro and …

Recent models of emotion recognition strongly rely on supervised deep learning solutions
for the distinction of general emotion expressions. However, they are not reliable when …

In this paper, we tackle the problem of predicting the affective responses of movie viewers,
based on the content of the movies. Current studies on this topic focus on video …

In this work, we propose different variants of the self-attention based network for emotion
prediction from movies, which we call AttendAffectNet. We take both audio and video into …