Unconsciously, humans evaluate situations based on environment and social parameters when recognizing emotions in social interactions. Without context, even humans may misunderstand the observed facial, vocal or body behavior. Contextual information, such as the ongoing task (e.g., human-computer vs. human-robot interaction), the identity (male vs. female) and natural expressiveness of the individual (e.g., introvert vs. extrovert), as well as the intra- and interpersonal contexts, help us to better interpret and respond to environment around us. These considerations suggest that attention to context information can deepen our understanding of affect communication (e.g., discrete emotions, affective dimensions such as valence and arousal, different types of moods and sentiment, etc.) for making reliable real-world affect-sensitive applications.
This 6th CBAR workshop aims to investigate how to efficiently exploit and model context using the cutting-edge computer vision and machine learning approaches in order to advance automatic affect recognitionTopics of interest include, but are not limited to:
  • Context-sensitive affect recognition from still images or videos.
  • Audio and/or physiological data modeling for context-sensitive affect recognition.
  • Context based corpora recording and annotation.
  • Domain adaptation for context-aware affect recognition.
  • Multi-modal context-aware fusion for affect recognition to successfully handle:
-Asynchrony and discordances of different modalities such as voice, face, and head/body.
-Innate priority among modalities.
-Temporal variations in the relative importance of the modalities according to the context.
  • Theoretical and empirical analysis of influence of context on affect recognition
  • Context aware applications:
-Depression severity assessment, pain intensity measurement, and autism screening (e.g. the influence of age, gender, intimate vs. stranger interaction, physician-patient relationship, home vs. hospital).
-Affect-based human-robot and human-embodied conversational agent interactions (e.g. autism therapy and story-telling, caregiving for the elderly).
-Other applications such as context-sensitive and affect-aware intelligent tutors (e.g. learning profile, personality assessment, student performance, content).

Submission Policy:
We call for submission of high-quality papers. The submitted manuscripts should not be submitted to another conference or workshop. Each paper will receive at least two reviews. Acceptance will be based on relevance to the workshop, novelty, and technical quality.

At least one author of each paper must register and attend the workshop to present the paper

Workshop Proceedings will be submitted for inclusion to IEEE Xplore.
Papers have to be submitted at the following link (EasyChair).  

The reviewing process for the workshop will bedouble-blind”. All submissions should, therefore, be appropriately anonymized not to reveal authors names or authorsinstitutions.

Submissions must be in PDF format, in accordance with the IEEE FG conference paper style (4-8 pages)

Keynote Speakers:
Title: Body movement as a rich modality to capture and regulate contextual pain experiences

Abstract: With the emergence of full-body sensing technology come new opportunities to support people’s affective experiences and needs. In my talk, I will present our work on technology for chronic pain management and discuss how such technology can lead to more effective physical rehabilitation through integrating it in everyday activities and supporting people at both physical and affective levels. I will also discuss our findings on how perception of pain behaviour is biased by context in human-human interaction and how affective-context modulates pain and pain coping capabilities. I will conclude by discussing some of the implications for affect- and pain-aware technology design.

Title: Interpersonal Human-Human and Human-Machine Interaction Approaches for Individual and Social Traits Detection

Abstract: One of the most significant challenges in robotics is to achieve closer interactions between humans and robots. Mutual behaviors occurring during interpersonal interaction provide unique insights into the complexities of the processes underlying human-robot coordination. In particular, interpersonal interaction, the process by which two or more people exchange information through verbal (what is said) and non-verbal (how it is said) messages could be exploited to both establish interaction and inform about the quality of interaction.

In this talk, we will report our recent works on social learning for (i) detecting individual traits such as pathology, identity and personality during human-robot interaction, (ii) engagement detection. We will also describe how these frameworks could be employed to investigate coordination mechanisms and in particular when it comes to pathologies such as autism spectrum disorders

Guoying Zhao 
Title: Facial expression analysis: From macro to micro

Abstract: Emotions are a central part of human communication, play an important role in everyday social life, and should have a key role in human-computer interactions. Emotions are complicated. Sometimes, people intentionally express their emotions, e.g., in the way of expressed macro-expressions, to help deliver the messages and sometimes people would suppress and hide their emotions, manipulated as micro-expressions, for different reasons. This talk introduces the work from macro-expression analysis, to micro-expression detection and recognition, and discusses the open problems in this area.

Tentative Schedule: Tuesday, 14th May 2019

Chair: Zakia Hammal

14:00 - 14:45: Keynote 1
Guoying Zhao
Facial expression analysis: From macro to micro.

14:45 - 15:30: Keynote 2
Nadia Berthouze
Body movement as a rich modality to capture and regulate contextual pain experiences.

15:30 - 16:00
Umut Avci (Yasar University) and Oya Aran (De La Salle University)
Analyzing group performance in small group interaction: Linking personality traits and group performance through the verbal content.

16:00 - 16:30: Coffee break

16:30 - 17:15: Keynote 3
Mohamed Chetouani
Interpersonal Human-Human and Human-Machine Interaction Approaches for Individual and Social Traits Detection.


Zakia Hammal
The Robotics Institute, 
Carnegie Mellon University.

Merlin Teodosia Suarez
Center for Empathic Human-Computer Interactions,
De La Salle University

Important Dates:
Submission Deadline: 28 January 2019
Notification of Acceptance:  17 February 2019
Camera Ready:                   24 February 2019

Program Committee (to be completed)
Anna Esposito, University degli Studi della Campania, “Luigi Vanvitelli”, Italy 
Mohammad H. Mahoor, University of Denver, USA 
Yan Tong, University of South Carolina, USA 
Ursula Hess, Humboldt University, Berlin  
Laurence Devillers, Paris-Sorbonne IV, France  
Hongying Meng, Brunel University London, UK
Oya Aran, De La Salle University, Philippines
Khiet Truong, University of Twente, Netherlands