Abstract
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 language | English |
|---|---|
| Title of host publication | Computer Vision - 14th European Conference, ECCV 2016, Proceedings |
| Editors | Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling |
| Publisher | Springer Verlag |
| Pages | 579-596 |
| Number of pages | 18 |
| ISBN (Print) | 9783319464534 |
| DOIs | |
| State | Published - 2016 |
| Event | 14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands Duration: 8 Oct 2016 → 16 Oct 2016 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9909 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 14th European Conference on Computer Vision, ECCV 2016 |
|---|---|
| Country/Territory | Netherlands |
| City | Amsterdam |
| Period | 8/10/16 → 16/10/16 |
Bibliographical note
Funding Information:The authors wish to thank Jongmoo Choi for all his help in this project. 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. Moreover, we gratefully acknowledge the support of NVIDIA with the donation of a Titan X.
Publisher Copyright:
© Springer International Publishing AG 2016.