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Reference TypeConference Paper (Conference Proceedings)
Last Update2015: (UTC)
Metadata Last Update2020: (UTC) administrator
Citation KeyLopesAguiOliv:2015:FaExRe
TitleA Facial Expression Recognition System Using Convolutional Networks
Access Date2021, Dec. 04
Number of Files1
Size1714 KiB
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Author1 Lopes, Andre Teixeira
2 Aguiar, Edilson de
3 Oliveira-Santos, Thiago
Affiliation1 Universidade Federal do Espírito Santo
2 Universidade Federal do Espírito Santo
3 Universidade Federal do Espírito Santo
EditorPapa, João Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-19 21:39:23 :: -> administrator ::
2020-02-19 02:14:04 :: administrator -> :: 2015
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Convolutional Networks
Computer Vision
Machine Learning
Expression Specific Features
AbstractFacial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods. > SDLA > SIBGRAPI 2015 > A Facial Expression...
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