1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPAW/3PF2Q9S |
Repository | sid.inpe.br/sibgrapi/2017/08.16.18.22 |
Last Update | 2017:09.28.14.15.08 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2017/08.16.18.22.12 |
Metadata Last Update | 2022:06.14.00.08.42 (UTC) administrator |
DOI | 10.1109/SIBGRAPI.2017.33 |
Citation Key | MartinsChiaFalc:2017:FaRoNe |
Title | A Fast and Robust Negative Mining Approach for Enrollment in Face Recognition Systems |
Format | On-line |
Year | 2017 |
Access Date | 2024, Apr. 27 |
Number of Files | 1 |
Size | 1073 KiB |
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2. Context | |
Author | 1 Martins, Samuel Botter 2 Chiachia, Giovani 3 Falcão, Alexandre Xavier |
Affiliation | 1 University of Campinas 2 University of Campinas 3 University of Campinas |
Editor | Torchelsen, Rafael Piccin Nascimento, Erickson Rangel do Panozzo, Daniele Liu, Zicheng Farias, Mylène Viera, Thales Sacht, Leonardo Ferreira, Nivan Comba, João Luiz Dihl Hirata, Nina Schiavon Porto, Marcelo Vital, Creto Pagot, Christian Azambuja Petronetto, Fabiano Clua, Esteban Cardeal, Flávio |
e-Mail Address | sbmmartins@ic.unicamp.br |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ, Brazil |
Date | 17-20 Oct. 2017 |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Full Paper |
History (UTC) | 2017-08-16 18:22:12 :: sbmmartins@ic.unicamp.br -> administrator :: 2017-09-12 13:05:31 :: administrator -> sbmmartins@ic.unicamp.br :: 2017 2017-09-28 14:15:08 :: sbmmartins@ic.unicamp.br -> administrator :: 2017 2022-06-14 00:08:42 :: administrator -> :: 2017 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
Keywords | face recognition negative mining convolutional networks |
Abstract | Consider a face image data set from clients of a company and the problem of building a face recognition system from it. Video cameras can be used to acquire several images per client in order to maximize the robustness of the system. However, as the data set grows huge, the accuracy of the system might be seriously compromised since the number of negative samples for each user is increasing. We propose here a first solution for this problem, which (i) limits the number of negative samples in the training set for preserving responsiveness during user enrollment, (ii) selects the most informative negative samples with respect to each user for preserving accuracy, and (iii) builds a user- specific classification model. We combine a high-dimensional data representation from deep learning with a method that selects negative samples from a large mining set and builds, within interactive times, effective user-specific training set and classifier, using linear support vector machines. The method can also be used with other feature extractors. It has shown superior performance as compared to five baseline methods on three unconstrained data sets. |
Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2017 > A Fast and... |
Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > A Fast and... |
doc Directory Content | access |
source Directory Content | PID4954541.pdf | 16/08/2017 15:22 | 1.2 MiB | |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPAW/3PF2Q9S |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PF2Q9S |
Language | en |
Target File | PID4954541.pdf |
User Group | sbmmartins@ic.unicamp.br |
Visibility | shown |
Update Permission | not transferred |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PKCC58 8JMKD3MGPEW34M/4742MCS |
Citing Item List | sid.inpe.br/sibgrapi/2017/09.12.13.04 5 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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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 |
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