1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPBW34M/3JMP3CL |
Repository | sid.inpe.br/sibgrapi/2015/06.19.21.39 |
Last Update | 2015:06.19.21.39.23 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2015/06.19.21.39.23 |
Metadata Last Update | 2022:06.14.00.08.12 (UTC) administrator |
DOI | 10.1109/SIBGRAPI.2015.14 |
Citation Key | LopesAguiOliv:2015:FaExRe |
Title | A Facial Expression Recognition System Using Convolutional Networks |
Format | On-line |
Year | 2015 |
Access Date | 2024, Sep. 20 |
Number of Files | 1 |
Size | 1714 KiB |
|
2. Context | |
Author | 1 Lopes, Andre Teixeira 2 Aguiar, Edilson de 3 Oliveira-Santos, Thiago |
Affiliation | 1 Universidade Federal do Espírito Santo 2 Universidade Federal do Espírito Santo 3 Universidade Federal do Espírito Santo |
Editor | Papa, João Paulo Sander, Pedro Vieira Marroquim, Ricardo Guerra Farrell, Ryan |
e-Mail Address | andreteixeiralopes@hotmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 28 (SIBGRAPI) |
Conference Location | Salvador, BA, Brazil |
Date | 26-29 Aug. 2015 |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Full Paper |
History (UTC) | 2015-06-19 21:39:23 :: andreteixeiralopes@hotmail.com -> administrator :: 2022-06-14 00:08:12 :: administrator -> :: 2015 |
|
3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
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. |
Arrangement | Fonds > Full Index > A Facial Expression... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
|
4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPBW34M/3JMP3CL |
zipped data URL | http://urlib.net/zip/8JMKD3MGPBW34M/3JMP3CL |
Language | en |
Target File | PID3755347.pdf |
User Group | andreteixeiralopes@hotmail.com |
Visibility | shown |
Update Permission | not transferred |
|
5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPBW34M/3K24PF8 8JMKD3MGPEW34M/4742MCS |
Citing Item List | sid.inpe.br/sibgrapi/2015/08.03.22.49 35 sid.inpe.br/sibgrapi/2022/06.10.21.49 3 sid.inpe.br/banon/2001/03.30.15.38.24 2 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
|
6. Notes | |
Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume |
|