6533b820fe1ef96bd127a2a9

RESEARCH PRODUCT

Eigenexpressions: Emotion Recognition Using Multiple Eigenspaces

Miguel Arevalillo-herráezLuis Marco-giménezCristina Cuhna-pérez

subject

EigenfaceFacial expression recognitionbusiness.industryComputer scienceEuclidean geometrySupervised learningComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionArtificial intelligenceEmotion recognitionbusinessClassifier (UML)Subspace topology

description

This paper presents an appearance-based holistic method for expression recognition. A two stage supervised learning approach is used. At the first stage, training images are used to compute one subspace per expression. At the second stage, the same images are used to train a classifier. In this step, Euclidean distances from each image to each particular subspace are used as the input to the classifier. The resulting system significantly outperforms the baseline eigenfaces method on the Cohn-Kanade data set, with performance gains in the range 10%-20%.

yearjournalcountryeditionlanguage
2013-01-01
https://doi.org/10.1007/978-3-642-38628-2_90