@InProceedings{6952941,
  author = {S. Aina and M. Zhou and J. A. Chambers and R. C. -. Phan},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {A new spontaneous expression database and a study of classification-based expression analysis methods},
  year = {2014},
  pages = {2505-2509},
  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.},
  keywords = {face recognition;image classification;image representation;principal component analysis;spontaneous expression database;classification-based expression analysis;open resource;expression classes;natural expression databases;kernel-based principal component analysis;PCA;Fisher linear discriminant analysis;FLDA;sparse representation classifier;SRC;Databases;Principal component analysis;Feature extraction;Kernel;Face recognition;Error analysis;Training;Fisher's Discriminant Analysis;Kernel;Principal Component;Sparsity;Spontaneous Expression Classification},
  issn = {2076-1465},
  month = {Sep.},
}

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