Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyMartinsChiaFalc:2017:FaRoNe
TitleA Fast and Robust Negative Mining Approach for Enrollment in Face Recognition Systems
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size1073 KiB
Context area
Author1 Martins, Samuel Botter
2 Chiachia, Giovani
3 Falcão, Alexandre Xavier
Affiliation1 University of Campinas
2 University of Campinas
3 University of Campinas
EditorTorchelsen, 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
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-16 18:22:12 :: -> administrator ::
2017-09-12 13:05:31 :: administrator -> :: 2017
2017-09-28 14:15:08 :: -> administrator :: 2017
2020-02-19 02:01:19 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsface recognition, negative mining, convolutional networks.
AbstractConsider 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.
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