Transactions on Affective Computing
IEEE Transactions on Affective Computing (TAC) is intended to be a cross disciplinary and international archive journal aimed at disseminating results of research on the design of systems that can recognize, interpret, and simulate human emotions and related affective phenomena. Read the full scope of TAC
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From the October-December 2018 issue
Multimodal Depression Detection: Fusion Analysis of Paralinguistic, Head Pose and Eye Gaze Behaviors
By Sharifa Alghowinem, Roland Goecke, Michael Wagner, Julien Epps, Matthew Hyett, Gordon Parker, and Michael Breakspear
An estimated 350 million people worldwide are affected by depression. Using affective sensing technology, our long-term goal is to develop an objective multimodal system that augments clinical opinion during the diagnosis and monitoring of clinical depression. This paper steps towards developing a classification system-oriented approach, where feature selection, classification and fusion-based experiments are conducted to infer which types of behaviour (verbal and nonverbal) and behaviour combinations can best discriminate between depression and non-depression. Using statistical features extracted from speaking behaviour, eye activity, and head pose, we characterise the behaviour associated with major depression and examine the performance of the classification of individual modalities and when fused. Using a real-world, clinically validated dataset of 30 severely depressed patients and 30 healthy control subjects, a Support Vector Machine is used for classification with several feature selection techniques. Given the statistical nature of the extracted features, feature selection based on T-tests performed better than other methods. Individual modality classification results were considerably higher than chance level (83 percent for speech, 73 percent for eye, and 63 percent for head). Fusing all modalities shows a remarkable improvement compared to unimodal systems, which demonstrates the complementary nature of the modalities. Among the different fusion approaches used here, feature fusion performed best with up to 88 percent average accuracy. We believe that is due to the compatible nature of the extracted statistical features.
Editorials and Announcements
- According to Clarivate Analytics' 2016 Journal Citation Report, TAC has an impact factor of 3.149.
- Heartfelt congratulations are offered to Georgios N. Yannakakis and Julian Togelius, authors of "Experience-Driven Procedural Content Generation," who were presented with TAC's Most Influential Paper Award by Editor-in-Chief Björn W. Schuller at the 2015 6th AAAC Affective Computing and Intelligent Interaction Conference in Xi'An, P.R. China on 22 September 2015.
- Special Issue/Section on Automated Perception of Human Affect from Longitudinal Behavioral Data
Submission Deadline: 15 January 2019
- Editorial: Transactions on Affective Computing – Good Reasons for Joy and Excitement (Jan-March 2018)
- Editorial: IEEE Transactions on Affective Computing – Challenges and Chances (Jan-March 2017)
- Editorial: Transactions on Affective Computing – Changes and Continuance (Jan-March 2016)
- Editorial: State of the Journal (July-Sept 2014)
- Introduction to TAC by J. Gratch
- Guest Editorial: Apparent Personality Analysis (July-Sept 2018)
- Towards Machines Able to Deal with Laughter (Oct-Dec 2017)
- Toward Commercial Applications of Affective Computing (April-June 2017)
- Best of Bodynets 2014: Editorial (July-Sept 2016)
- Challenges and Perspectives for Affective Analysis in Multimedia (July-Sept 2015)
- Introduction to the "Best of ACII 2013" Special Section (April-June 2015)
- Affect and Wellbeing: Introduction to Special Section (July-Sept 2014)
- Editorial for the Special Section on Ethics and Affective Computing
- Introduction to the Affect-Based Human Behavior Understanding Special Issue
- Affective Computing: From Laughter to IEEE by R.W. Picard
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