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Image Quality Assessment Using Similar Scene as Reference Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Do We Really Need to Collect Millions of Faces for Effective Face Recognition?

Authors : Iacopo Masi, Anh Tuấn Trần, Tal Hassner, Jatuporn Toy Leksut, Gérard Medioni

Published in: Computer Vision – ECCV 2016

Publisher: Springer International Publishing

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Abstract

Face recognition capabilities have recently made extraordinary 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 specific data augmentation. We describe novel methods of enriching an existing 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.

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previous chapter Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
next chapter Generative Visual Manipulation on the Natural Image Manifold
Footnotes
1
MegaFace [14] is larger than CASIA, but was designed as a testing set and so provides few images per subject. It was consequently never used for training CNN systems.
 
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Metadata
Title
Do We Really Need to Collect Millions of Faces for Effective Face Recognition?
Authors
Iacopo Masi
Anh Tuấn Trần
Tal Hassner
Jatuporn Toy Leksut
Gérard Medioni
Copyright Year
2016
Book
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46453-4

Electronic ISBN: 978-3-319-46454-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46454-1_35

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