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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Maximiano da Silva, Flavio Altinier [1] ; Pedrini, Helio [1]
Total Authors: 2
[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Web of Science Citations: 4

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. (C) 2015 SPIE and IS\&T (AU)

FAPESP's process: 14/04020-9 - Active appearance model applied to emotion recognition through partially occluded facial expression
Grantee:Flavio Altinier Maximiano da Silva
Support Opportunities: Scholarships in Brazil - Scientific Initiation
FAPESP's process: 11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications
Grantee:Luis Gustavo Nonato
Support Opportunities: Research Projects - Thematic Grants