Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2018:
Metadata Last Update2020: administrator
Citation KeyMasiWuHassNata:2018:DeFaRe
TitleDeep Face Recognition: a Survey
DateOct. 29 - Nov. 1, 2018
Access Date2021, Jan. 19
Number of Files1
Size395 KiB
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Author1 Masi, Iacopo
2 Wu, Yue
3 Hassner, Tal
4 Natarajan, Prem
Affiliation1 Information Sciences Institute (ISI), University of Southern California (USC)
2 Information Sciences Institute (ISI), University of Southern California (USC)
3 The Open University of Israel, Raanana, Israel
4 Information Sciences Institute (ISI), University of Southern California (USC)
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeTutorial
History2018-09-10 22:41:31 :: -> administrator ::
2020-02-20 22:06:48 :: administrator -> :: 2018
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Is the master or a copy?is the master
Content Stagecompleted
Keywordsface recognition, deep learning, survey.
AbstractFace recognition made tremendous leaps in the last five years with a myriad of systems proposing novel techniques substantially backed by deep convolutional neural networks (DCNN). Although face recognition performance sky-rocketed using deep-learning in classic datasets like LFW, leading to the belief that this technique reached human performance, it still remains an open problem in unconstrained environments as demonstrated by the newly released IJB datasets. This survey aims to summarize the main advances in deep face recognition and, more in general, in learning face representations for verification and identification. The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues. The survey is broken down into multiple parts that follow a standard face recognition pipeline: (a) how SOTA systems are trained and which public data sets have they used; (b) face preprocessing part (detection, alignment, etc.); (c) architecture and loss functions used for transfer learning (d) face recognition for verification and identification. The survey concludes with an overview of the SOTA results at a glance along with some open issues currently overlooked by the community.
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