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)


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
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

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


ConferenceEuropean Signal Processing Conference 2014
Abbreviated titleEUSIPCO 2014
Other2014 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO)
Internet address


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

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