The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Do We Really Need to Collect Millions of Faces for Effective Face Recognition?
Open accessAbstract
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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Cited by
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781
Deep Facial Expression Recognition: A Survey
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Improving Deep Learning with Generic Data Augmentation
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Past, Present, and Future of Face Recognition: A Review
Adjabi, I., Ouahabi, A., Benzaoui, A., & Taleb-Ahmed, A. (2020) · Electronics · doi:10.3390/electronics9081188 · Cited by 560
Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network
Tran, A. T., Hassner, T., Masi, I., & Medioni, G. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.163 · Cited by 503
Self-citation
Metadata
- Authors
- Masi, I., Trần, A. T., Hassner, T., Leksut, J. T., & Medioni, G.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 579-596
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2304348237
- Cited by
- 339
- References
- 44
- FWCI
- 22.55
- Type
- conference paper
- Language
- en
- Open access
- green
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