2018
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Kloss, Ricardo Barbosa; Jordao, Artur; Schwartz, William Robson Face Verification Strategies for Employing Deep Models Inproceedings Em: 13th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 258-262, 2018. @inproceedings{Kloss:2018:FG,
title = {Face Verification Strategies for Employing Deep Models},
author = {Ricardo Barbosa Kloss and Artur Jordao and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Face-Verification-Strategies-for-Employing-Deep-Models.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {13th IEEE International Conference on Automatic Face & Gesture Recognition},
pages = {258-262},
keywords = {Artificial Neural Networks, Face Verification, GigaFrames, Metric Learning, Transfer Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
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2017
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Vareto, Rafael Henrique Face Recognition based on a Collection of Binary Classifiers Masters Thesis Federal University of Minas Gerais, 2017. @mastersthesis{Vareto:2017:MSc,
title = {Face Recognition based on a Collection of Binary Classifiers},
author = {Rafael Henrique Vareto},
year = {2017},
date = {2017-10-16},
school = {Federal University of Minas Gerais},
abstract = {Face Recognition is one of the most relevant problems in computer vision as we consider its importance to areas such as surveillance, forensics and psychology. In fact, a real-world recognition system has to cope with several unseen individuals and determine either if a given face image is associated with a subject registered in a gallery of known individuals or if two given faces represent equivalent identities. In this work, not only we combine hashing functions, embedding of classifiers and response value histograms to estimate when probe samples belong to the gallery set, but we also extract relational features to model the relation between pair of faces to determine whether they are from the same person. Both proposed methods are evaluated on five datasets: FRGCv1, LFW, PubFig, PubFig83 and CNN VGGFace. Results are promising and show that our method continues effective for both open-set face identification and verification tasks regardless of the dataset difficulty.},
keywords = {Artificial Neural Network, Face Verification, Machine Learning, Open-set Face Identification, Partial Least Squares, Support Vector Machine, Surveillance},
pubstate = {published},
tppubtype = {mastersthesis}
}
Face Recognition is one of the most relevant problems in computer vision as we consider its importance to areas such as surveillance, forensics and psychology. In fact, a real-world recognition system has to cope with several unseen individuals and determine either if a given face image is associated with a subject registered in a gallery of known individuals or if two given faces represent equivalent identities. In this work, not only we combine hashing functions, embedding of classifiers and response value histograms to estimate when probe samples belong to the gallery set, but we also extract relational features to model the relation between pair of faces to determine whether they are from the same person. Both proposed methods are evaluated on five datasets: FRGCv1, LFW, PubFig, PubFig83 and CNN VGGFace. Results are promising and show that our method continues effective for both open-set face identification and verification tasks regardless of the dataset difficulty. |
Vareto, Rafael Henrique; da Silva, Samira Santos; de Costa, Filipe Oliveira; Schwartz, William Robson Face Verification based on Relational Disparity Features and Partial Least Squares Models Inproceedings Em: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2017. @inproceedings{Vareto:2017:SIBGRAPI,
title = {Face Verification based on Relational Disparity Features and Partial Least Squares Models},
author = {Rafael Henrique Vareto and Samira Santos da Silva and Filipe Oliveira de Costa and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2017_SIBGRAPI_Vareto.pdf
http://lena.dcc.ufmg.br/wordpress/face-verification-based-relational-disparity-features-partial-least-squares-models/},
year = {2017},
date = {2017-01-01},
booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)},
pages = {1-8},
keywords = {Face Recognition, Face Verification},
pubstate = {published},
tppubtype = {inproceedings}
}
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2012
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Chiachia, Giovani; Pinto, Nicolas; Schwartz, William Robson; Rocha, Anderson; ao, Alexandre X Falc; Cox, David D Person-Specific Subspace Analysis for Unconstrained Familiar Face Identification. Inproceedings Em: BMVC, pp. 1–12, 2012. @inproceedings{chiachia2012person,
title = {Person-Specific Subspace Analysis for Unconstrained Familiar Face Identification.},
author = {Giovani Chiachia and Nicolas Pinto and William Robson Schwartz and Anderson Rocha and Alexandre X Falc~ao and David D Cox},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2012-Person-Specific-Subspace-Analysis-for-Unconstrained-Familiar-Face-Identification..pdf},
year = {2012},
date = {2012-01-01},
booktitle = {BMVC},
pages = {1--12},
keywords = {Face Recognition, Face Verification, Partial Least Squares},
pubstate = {published},
tppubtype = {inproceedings}
}
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2011
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Guo, H; Schwartz, William Robson; Davis, L S Face Verification using Large Feature Sets and One Shot Similarity Inproceedings Em: International Joint Conference on Biometrics, 2011. @inproceedings{Guo:2011:IJCB,
title = {Face Verification using Large Feature Sets and One Shot Similarity},
author = {H Guo and William Robson Schwartz and L S Davis},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2011-Face-Verification-using-Large-Feature-Sets-and-One-Shot-Similarity.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {International Joint Conference on Biometrics},
keywords = {Face Recognition, Face Verification, One Shot Similarity},
pubstate = {published},
tppubtype = {inproceedings}
}
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