A new spontaneous expression database and a study of classification-based expression analysis methods

Segun Aina, Mingxi Zhou, Jonathon A. Chambers, Raphael C.W. Phan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)

Abstract

In this paper we introduce a new spontaneous expression database, which is under development as a new open resource for researchers working in expression analysis. It is particularly targeted at providing a wider number of expression classes contained within the small number of natural expression databases currently available so that it can be used as a benchmark for comparative studies. We also present the first comparison between kernel-based Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (FLDA), in combination with a Sparse Representation Classifier (SRC), based classifier for expression analysis. We highlight the trade-off between performance and computation time; which are critical parameters in emerging systems which must capture the expression of a human, such as a consumer responding to some promotional material.

Original languageEnglish
Title of host publication2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages2505-2509
Number of pages5
ISBN (Electronic)9780992862619
Publication statusPublished - 10 Nov 2014
Externally publishedYes
EventEuropean Signal Processing Conference 2014 - Lisbon, Portugal
Duration: 1 Sep 20145 Sep 2014
Conference number: 22nd
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6937054

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

ConferenceEuropean Signal Processing Conference 2014
Abbreviated titleEUSIPCO 2014
CountryPortugal
CityLisbon
Period1/09/145/09/14
Other2014 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO)
Internet address

Keywords

  • Fisher's Discriminant Analysis
  • Kernel
  • Principal Component
  • Sparsity
  • Spontaneous Expression Classification

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