Learning CNNs for face recognition from weakly annotated images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00312262" target="_blank" >RIV/68407700:21230/17:00312262 - isvavai.cz</a>
Result on the web
<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/franc/Franc-EMSVM-FG2017.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/franc/Franc-EMSVM-FG2017.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/FG.2017.115" target="_blank" >10.1109/FG.2017.115</a>
Alternative languages
Result language
angličtina
Original language name
Learning CNNs for face recognition from weakly annotated images
Original language description
Supervised learning of convolutional neural networks (CNNs) for face recognition requires a large set of facial images each annotated with a single attribute label to be predicted. In this paper we propose a method for learning CNNs from weakly annotated images. The weak annotation in our setting means that a pair of an attribute label and a person identity label is assigned to a set of faces automatically detected in the image. The challenge is to link the annotation with the correct face. The weakly annotated images of this type can be collected by an automated process not requiring a human labor. We formulate learning from weakly annotated images as a maximum likelihood estimation of a parametric distribution describing the data. The ML problem is solved by an instance of EM algorithm which in its inner loop learns a CNN to perform given face recognition task. Experiments on age and gender estimation problem show that the proposed EM-CNN algorithm significantly outperforms the state-of-the-art approach for dealing with this type of data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
International Conference on Automatic Face and Gesture Recognition Workshops, Biometrics in the Wild
ISBN
978-1-5090-4023-0
ISSN
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e-ISSN
2326-5396
Number of pages
8
Pages from-to
933-940
Publisher name
IEEE Computer Society
Place of publication
USA
Event location
Washington DC
Event date
May 30, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000414287400129