Dynamic behavior analysis via structured rank minimization
File(s)
Published version
Author(s)
Georgakis, C
Panagakis, Y
Pantic, M
Type
Journal Article
Abstract
Human behavior and affect is inherently a dynamic phenomenon involving temporal evolution of patterns manifested through a multiplicity of non-verbal behavioral cues including facial expressions, body postures and gestures, and vocal outbursts. A natural assumption for human behavior modeling is that a continuous-time characterization of behavior is the output of a linear time-invariant system when behavioral cues act as the input (e.g., continuous rather than discrete annotations of dimensional affect). Here we study the learning of such dynamical system under real-world conditions, namely in the presence of noisy behavioral cues descriptors and possibly unreliable annotations by employing structured rank minimization. To this end, a novel structured rank minimization method and its scalable variant are proposed. The generalizability of the proposed framework is demonstrated by conducting experiments on 3 distinct dynamic behavior analysis tasks, namely (i) conflict intensity prediction, (ii) prediction of valence and arousal, and (iii) tracklet matching. The attained results outperform those achieved by other state-of-the-art methods for these tasks and, hence, evidence the robustness and effectiveness of the proposed approach.
Date Issued
2017-01-19
Date Acceptance
2016-12-21
Citation
International Journal of Computer Vision, 2017, 126 (2-4), pp.333-357
ISSN
0920-5691
Publisher
Springer
Start Page
333
End Page
357
Journal / Book Title
International Journal of Computer Vision
Volume
126
Issue
2-4
Copyright Statement
© 2017 The Author(s). Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Sponsor
Commission of the European Communities
Grant Number
645094
Subjects
0801 Artificial Intelligence And Image Processing
Artificial Intelligence & Image Processing
Publication Status
Published