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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/09.06.13.12
%2 sid.inpe.br/sibgrapi/2017/09.06.13.12.02
%T Facial Expression Recognition: Comparison of Feature Extraction Methods
%D 2017
%A Kubo, Diandra Akemi Alves,
%A Bellon, Olga Regina Pereira,
%A Flynn, Patrick,
%A Silva, Luciano,
%@affiliation Universidade Federal do Paraná
%@affiliation Universidade Federal do Paraná
%@affiliation University of Notre Dame
%@affiliation Universidade Federal do Paraná
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ
%8 Oct. 17-20, 2017
%S Proceedings
%I Sociedade Brasileira de Computação
%J Porto Alegre
%K facial expression, feature extraction.
%X Human facial expressions are one of the most im-portant communication channels, being used with trust to betterunderstand ones state of mind in a variety of applications, forinstance, emotion recognition. As a result, various algorithms andmethods have been developed for facial expression recognition.On this context, we review the literature and conduct tests ondifferent algorithms regarding facial feature extraction, in orderto evaluate their performance on the BU-3DFE database. Thisdatabase was chosen because it is widely used and all emotionsare annotated for each image. Therefore BU-3DFE is suitable forthe proposed benchmarking. The best result was achieved by acombination of Eigenfaces and SVM as classifier.
%@language en
%3 2017_sibgrapi_daakubo.pdf


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