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

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.