@InProceedings{LopesAguiOliv:2015:FaExRe,
author = "Lopes, Andre Teixeira and Aguiar, Edilson de and Oliveira-Santos,
Thiago",
affiliation = "{Universidade Federal do Esp{\'{\i}}rito Santo} and
{Universidade Federal do Esp{\'{\i}}rito Santo} and
{Universidade Federal do Esp{\'{\i}}rito Santo}",
title = "A Facial Expression Recognition System Using Convolutional
Networks",
booktitle = "Proceedings...",
year = "2015",
editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim,
Ricardo Guerra and Farrell, Ryan",
organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Expression, Convolutional Networks, Computer Vision, Machine
Learning, Expression Specific Features.",
abstract = "Facial 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.",
conference-location = "Salvador, BA, Brazil",
conference-year = "26-29 Aug. 2015",
doi = "10.1109/SIBGRAPI.2015.14",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2015.14",
language = "en",
ibi = "8JMKD3MGPBW34M/3JMP3CL",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JMP3CL",
targetfile = "PID3755347.pdf",
urlaccessdate = "2024, Apr. 28"
}