Do we really need to collect millions of faces for effective face recognition?

Iacopo Masi, Anh Tuân Trân, Tal Hassner, Jatuporn Toy Leksut, Gérard Medioni

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


Face recognition capabilities have recently made extraordi- nary leaps. Though this progress is at least partially due to ballooning training set sizes - huge numbers of face images downloaded and labeled for identity - it is not clear if the formidable task of collecting so many images is truly necessary. We propose a far more accessible means of increasing training data sizes for face recognition systems: Domain spe- cific data augmentation. We describe novel methods of enriching an exist- ing dataset with important facial appearance variations by manipulating the faces it contains. This synthesis is also used when matching query images represented by standard convolutional neural networks. The effect of training and testing with synthesized images is tested on the LFW and IJB-A (verification and identification) benchmarks and Janus CS2. The performances obtained by our approach match state of the art results reported by systems trained on millions of downloaded images.

Original languageEnglish
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsBastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
PublisherSpringer Verlag
Number of pages18
ISBN (Print)9783319464534
StatePublished - 2016
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: 8 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9909 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th European Conference on Computer Vision, ECCV 2016

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.


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