@InProceedings{MartinsChiaFalc:2017:FaRoNe,
author = "Martins, Samuel Botter and Chiachia, Giovani and Falc{\~a}o,
Alexandre Xavier",
affiliation = "{University of Campinas} and {University of Campinas} and
{University of Campinas}",
title = "A Fast and Robust Negative Mining Approach for Enrollment in Face
Recognition Systems",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
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.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.33",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.33",
language = "en",
ibi = "8JMKD3MGPAW/3PF2Q9S",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PF2Q9S",
targetfile = "PID4954541.pdf",
urlaccessdate = "2024, Apr. 27"
}