Behavior prediction in-the-wild

Conference paper


Georgakis, C., Panagakis, Y. and Pantic, M. 2017. Behavior prediction in-the-wild. 2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). San Antonio, TX, USA, 2017 23 - 26 Oct 2017 Institute of Electrical and Electronics Engineers (IEEE). pp. 18-25 https://doi.org/10.1109/ACIIW.2017.8272617
TypeConference paper
TitleBehavior prediction in-the-wild
AuthorsGeorgakis, C., Panagakis, Y. and Pantic, M.
Abstract

In this paper, the problem of audio-visual behavior prediction in-the-wild is addressed. In this context, both audio-visual descriptors of behavioral cues (features) and continuous-time real-valued characterizations of behavior (annotations) are (possibly) corrupted by non-Gaussian noise of large magnitude. The modeling assumption behind the proposed framework is that naturalistic affect and behavior captured in audio-visual episodes are smoothly-varying dynamic phenomena and thus the hidden temporal dynamics can be modeled as a generative auto-regressive process. Consequently, continuous-time real-valued characterizations of behavior (annotations) are postulated to be outputs of a low-complexity (i.e., low-order) time-invariant Linear Dynamical System (LDS) when descriptors of behavioral cues (features) act as inputs. To learn the parameters of the LDS, a recently proposed spectral method that relies on Hankel-rank minimization is adopted. Experimental evaluation on a challenging database recorded in the wild demonstrate the effectiveness of the proposed approach in behavior prediction.

LanguageEnglish
Conference2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)
Page range18-25
ISBN
Hardcover9781538606803
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication dates
Print26 Oct 2017
Online01 Feb 2018
Publication process dates
Deposited06 Mar 2018
Accepted01 Oct 2017
Output statusPublished
Accepted author manuscript
Copyright Statement

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Digital Object Identifier (DOI)https://doi.org/10.1109/ACIIW.2017.8272617
Book title2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)
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