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 July-September 2018 issue
Deep Bimodal Regression of Apparent Personality Traits from Short Video Sequences
By Xiu-Shen Wei, Chen-Lin Zhang, Hao Zhang, and Jianxin Wu
Apparent personality analysis (APA) is an important problem of personality computing, and furthermore, automatic APA becomes a hot and challenging topic in computer vision and multimedia. In this paper, we propose a deep learning solution to APA from short video sequences. In order to capture rich information from both the visual and audio modality of videos, we tackle these tasks with our Deep Bimodal Regression (DBR) framework. In DBR, for the visual modality, we modify the traditional convolutional neural networks for exploiting important visual cues. In addition, taking into account the model efficiency, we extract audio representations and build a linear regressor for the audio modality. For combining the complementary information from the two modalities, we ensemble these predicted regression scores by both early fusion and late fusion. Finally, based on the proposed framework, we come up with a solution for the Apparent Personality Analysis competition track in the ChaLearn Looking at People challenge in association with ECCV 2016. Our DBR is the winner (first place) of this challenge with 86 registered participants. Beyond the competition, we further investigate the performance of different loss functions in our visual models, and prove non-convex loss functions for regression are optimal on the human-labeled video data.
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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