Virtual sample generation using concurrent-self-organizing maps and its application for facial expression recognition

Authors:
Victor-Emil Neagoe;Adrian-Dumitru Ciotec
Affiliations:
Polytechnic University of Bucharest, Romania;Polytechnic University of Bucharest, Romania
Venue:
MMES'10 Proceedings of the 2010 international conference on Mathematical models for engineering science
Year:
2010

Citing 11
Cited 1

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Abstract

This paper is dedicated to the improvement of a pattern classifier generalization performances. One proposes the increasing of the training set size, by means of "virtual" sample generation using a set of concurrent self-organizing maps (VSG-CSOM). We have evaluated the above proposed model for facial expression recognition. One uses Japanese female facial expression (JAFFE) database corresponding to seven emotion classes: happiness, sadness, surprise, anger, disgust, fear and neutral face. We have considered the following classifiers: nearest neighbour (NN), multilayer perceptron (MLP), and radial basis function (RBF) neural classifier. One obtains an obvious improvement in generalization performances for all the considered statistical/neural classifiers. For example, the recognition score evaluated on the test set as a consequence of virtual sample generation increases for the MLP from 67.14 % to 92.86 % and for the RBF from 87.14 % to 94.29 %.