[visionlist] CFP: TAC | Special Issue on Automated Perception of Human Affect from Longitudinal Behavioral Data

Pablo Barros barros at informatik.uni-hamburg.de
Mon Jan 7 06:10:46 -05 2019


Extended Deadline for the IEEE Transactions on Affective Computing
Special Issue on Automated Perception of Human Affect from Longitudinal
Behavioral Data


 I. Aim and Scope

Research trends within artificial intelligence and cognitive sciences are
still heavily based on computational models that attempt to imitate human
perception in various behavior categorization tasks. However, most of the
research in the field focuses on instantaneous categorization and
interpretation of human affect, such as the inference of six basic emotions
from face images, and/or affective dimensions (valence-arousal), stress and
engagement from multi-modal (e.g., video, audio, and autonomic physiology)
data. This diverges from the developmental aspect of emotional behavior
perception and learning, where human behavior and expressions of affect
evolve and change over time. Moreover, these changes are present not only
in the temporal domain but also within different populations and more
importantly, within each individual. This calls for a new perspective when
designing computational models for analysis and interpretation of human
affective behaviors: the computational models that can timely and
efficiently adapt to different contexts and individuals over time, and also
incorporate existing neurophysiological and psychological findings (prior
knowledge). Thus, the long-term goal is to create life-long personalized
learning and inference systems for analysis and perception of human
affective behaviors. Such systems would benefit from long-term contextual
information (including demographic and social aspects) as well as
individual characteristics. This, in turn, would allow building intelligent
agents (such as mobile and robot technologies) capable of adapting their
behavior in a continuous and on-line manner to the target contexts and

This special issue aims at contributions from computational neuroscience
and psychology, artificial intelligence, machine learning, and affective
computing, challenging and expanding current research on interpretation and
estimation of human affective behavior from longitudinal behavioral data,
i.e., single or multiple modalities captured over extended periods of time
allowing efficient profiling of target behaviors and their inference in
terms of affect and other socio-cognitive dimensions. We invite
contributions focusing on both the theoretical and modeling perspective, as
well as applications ranging from human-human, human-computer and
human-robot interactions.

II. Potential Topics

Given computational models, the capability to perceive and understand
emotion behavior is an important and popular research topic. That is why
recent special issues on the IEEE Journal on Transactions on Affective
Computing covered topics from emotion behavior analysis “in-the-wild” to
personality analysis. However, most of the research published by these
specific calls treat emotion behavior as an instantaneous event, relating
mostly to emotion recognition, and thus neglect the development of complex
emotion behavior models. Our special issue will foster the development of
the field by focusing excellent research on emotion models for long-term
behavior analysis.

The topics of interest for this special issue include, but are not limited

- New theories and findings on continuous emotion recognition
- Multi- and Cross-modal emotion perception and interpretation
- Lifelong affect analysis, perception, and interpretation
- Novel neural network models for affective processing
- New neuroscientific and psychological findings on continuous emotion
- Embodied artificial agents for empathy and emotion appraisal
- Machine learning for affect-driven interventions
- Socially intelligent human-robot interaction
- Personalized systems for human affect recognition

III. Submission

Prospective authors are invited to submit their manuscripts electronically,
adhering to the IEEE Transactions on Affective Computing guidelines (
https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165369). Please
submit your papers through the online system (
https://mc.manuscriptcentral.com/taffc-cs) and be sure to select the
special issue: Special Issue/Section on Automated Perception of Human
Affect from Longitudinal Behavioral Data.


Submissions Deadline: 15th of February 2019

V. Guest Editors

Pablo Barros, University of Hamburg, Germany
Stefan Wermter, University of Hamburg, Germany
Ognjen (Oggi) Rudovic, Massachusetts Institute of Technology, United States
of America
Hatice Gunes, University of Cambridge, United Kingdom


Dr. Pablo Barros
Postdoctoral Research Associate - Crossmodal Learning Project (CML)
Knowledge Technology
Department of Informatics
University of Hamburg
Vogt-Koelln-Str. 30
22527 Hamburg, Germany
Phone: +49 40 42883 2535
Fax: +49 40 42883 2515
barros at informatik.uni-hamburg.dehttp://www.pablobarros.nethttps://www.inf.uni-hamburg.de/en/inst/ab/wtm/people/barros.htmlhttps://www.inf.uni-hamburg.de/en/inst/ab/wtm/
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