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Journal of Electronic Imaging

Effects of cultural characteristics on building an emotion classifier through facial expression analysis
Author(s): Flávio Altinier Maximiano da Silva; Helio Pedrini
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Paper Abstract

Facial expressions are an important demonstration of humanity’s humors and emotions. Algorithms capable of recognizing facial expressions and associating them with emotions were developed and employed to compare the expressions that different cultural groups use to show their emotions. Static pictures of predominantly occidental and oriental subjects from public datasets were used to train machine learning algorithms, whereas local binary patterns, histogram of oriented gradients (HOGs), and Gabor filters were employed to describe the facial expressions for six different basic emotions. The most consistent combination, formed by the association of HOG filter and support vector machines, was then used to classify the other cultural group: there was a strong drop in accuracy, meaning that the subtle differences of facial expressions of each culture affected the classifier performance. Finally, a classifier was trained with images from both occidental and oriental subjects and its accuracy was higher on multicultural data, evidencing the need of a multicultural training set to build an efficient classifier.

Paper Details

Date Published: 19 March 2015
PDF: 9 pages
J. Electron. Imaging. 24(2) 023015 doi: 10.1117/1.JEI.24.2.023015
Published in: Journal of Electronic Imaging Volume 24, Issue 2
Show Author Affiliations
Flávio Altinier Maximiano da Silva, Univ. Estadual de Campinas (Brazil)
Helio Pedrini, Univ. Estadual de Campinas (Brazil)

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