Deep Face Recognition: A Survey

Iacopo Masi, Yue Wu, Tal Hassner, Prem Natarajan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Face 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.

Original languageEnglish
Title of host publicationProceedings - 31st Conference on Graphics, Patterns and Images, SIBGRAPI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781538692646
StatePublished - 2 Jul 2018
Event31st Conference on Graphics, Patterns and Images, SIBGRAPI 2018 - Foz do Iguacu, Brazil
Duration: 29 Oct 20181 Nov 2018

Publication series

NameProceedings - 31st Conference on Graphics, Patterns and Images, SIBGRAPI 2018


Conference31st Conference on Graphics, Patterns and Images, SIBGRAPI 2018
CityFoz do Iguacu

Bibliographical note

Funding Information:
This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA 2014-14071600011. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon.

Publisher Copyright:
© 2018 IEEE.


  • deep learning
  • face recognition
  • survey


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