Effects of cultural characteristics on building an emotion classifier through facial expression analysis

Flávio Altinier Maximiano da Silva; Helio Pedrini

doi:10.1117/1.JEI.24.2.023015

19 March 2015 Effects of cultural characteristics on building an emotion classifier through facial expression analysis

Flávio Altinier Maximiano da Silva, Helio Pedrini

Author Affiliations +

Funded by: FAPESP, FAPESP—São Paulo Research Foundation, CNPq

Journal of Electronic Imaging, Vol. 24, Issue 2, 023015 (March 2015). https://doi.org/10.1117/1.JEI.24.2.023015

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.

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Flávio Altinier Maximiano da Silva and Helio Pedrini "Effects of cultural characteristics on building an emotion classifier through facial expression analysis," Journal of Electronic Imaging 24(2), 023015 (19 March 2015). https://doi.org/10.1117/1.JEI.24.2.023015

Published: 19 March 2015

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