Probabilistic slow features for behavior analysis
File(s)
Accepted version
Author(s)
Zafeiriou, L
Nicolaou, MA
Zafeiriou, S
Nikitidis, S
Pantic, M
Type
Journal Article
Abstract
A recently introduced latent feature learning technique for time-varying dynamic phenomena analysis is the so-called slow feature analysis (SFA). SFA is a deterministic component analysis technique for multidimensional sequences that, by minimizing the variance of the first-order time derivative approximation of the latent variables, finds uncorrelated projections that extract slowly varying features ordered by their temporal consistency and constancy. In this paper, we propose a number of extensions in both the deterministic and the probabilistic SFA optimization frameworks. In particular, we derive a novel deterministic SFA algorithm that is able to identify linear projections that extract the common slowest varying features of two or more sequences. In addition, we propose an expectation maximization (EM) algorithm to perform inference in a probabilistic formulation of SFA and similarly extend it in order to handle two and more time-varying data sequences. Moreover, we demonstrate that the probabilistic SFA (EM-SFA) algorithm that discovers the common slowest varying latent space of multiple sequences can be combined with dynamic time warping techniques for robust sequence time-alignment. The proposed SFA algorithms were applied for facial behavior analysis, demonstrating their usefulness and appropriateness for this task.
Date Issued
2015-06-09
Date Acceptance
2015-05-16
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2015, 27 (5), pp.1034-1048
ISSN
2162-2388
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
1034
End Page
1048
Journal / Book Title
IEEE Transactions on Neural Networks and Learning Systems
Volume
27
Issue
5
Copyright Statement
© 2015 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.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
Grant Number
EP/J017787/1
EP/L026813/1
645094
Publication Status
Published