Behavior prediction in-the-wild

Georgakis, Christos, Panagakis, Yannis and Pantic, Maja (2017) Behavior prediction in-the-wild. In: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), 23-26 Oct 2017, San Antonio, TX, USA, 2017.

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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.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 23782
Notes on copyright: © 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.
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Depositing User: Yannis Panagakis
Date Deposited: 06 Mar 2018 18:14
Last Modified: 04 Apr 2019 06:36

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