Affective computing has an unprecedented potential to change the way humans interact with
technology. While the last decades have witnessed vast progress in the field, multimodal …

Artificial intelligence (AI) has rapidly developed through advancements in computational
power and the growth of massive datasets. However, this progress has also heightened …

We introduce the MuSe-Toolbox-a Python-based open-source toolkit for creating a variety of
continuous and discrete emotion gold standards. In a single framework, we unify a wide …

How could we gather affect annotations in a rapid, unobtrusive, and accessible fashion?
How could we still make sure that these annotations are reliable enough for data-hungry …

Time-continuous dimensional descriptions of emotions (eg, arousal, valence) allow
researchers to characterize short-time changes and to capture long-term trends in emotion …

Automatic emotion recognition plays a key role in human-computer interactions. Multimodal
emotion recognition has attracted much attention in recent years. When multimodalities are …

We present a dataset (IMPRESSION) for multi-modal recognition of impressions on
individuals and dyads. Compared to other databases, we did not only elicit impression using …

Automated recognition of continuous emotions in audio-visual data is a growing area of
study that aids in understanding human-machine interaction. Training such systems …

In the recent years, there has been a shift in facial behavior analysis from the laboratory-
controlled conditions to the challenging in-the-wild conditions due to the superior …

The explanation of the identified outliers is frequently provided to the users when other
techniques are used to detect the outliers that are suggested in the study. This situation is …