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Face detection and recognition benchmarks have shifted toward more difficult environments.
Discriminant analysis of principal components for face recognition
W. Zhao, A. Krishnaswamy, R. Chellappa, D. L. Swets, and J. Weng · 1998
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Statistical power in observer-performance studies: Comparison of the receiver operating characteristic and free-response methods in tasks involving localization
D. Chakraborty · 2002
Earlier work this paper cites.
Face recognition with local binary patterns
T. Ahonen, A. Hadid, and M. Pietikainen · 2004
Earlier work this paper cites.
The Pascal visual object classes (VOC) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
SCface — surveillance cameras face database
M. Grgic, K. Delac, and S. Grgic · 2011
Earlier work this paper cites.
Handbook of Face Recognition
P. J. Phillips, P. Grother, and R. Micheals · 2011
Earlier work this paper cites.
Bob: a free signal processing and machine learning toolbox for researchers
A. Anjos, L. E. Shafey, R. Wallace, M. Günther, C. McCool, and S. Marcel · 2012
Earlier work this paper cites.
Multivariate Boosting with Look-up Tables for Face Processing
C. Atanasoaei · 2012
Earlier work this paper cites.
Bi-modal person recognition on a mobile phone: using mobile phone data
C. McCool, S. Marcel, A. Hadid, M. Pietikainen, P. Matejka, J. Cernocky, N. Poh, J. Kittler, A. Larcher, C. Levy, D. Matrouf, J.-F. Bonastre, P. Tresadern, and T. Cootes · 2012
Earlier work this paper cites.
The challenge of face recognition from digital point-and-shoot cameras
J. R. Beveridge, P. J. Phillips, D. S. Bolme, B. A. Draper, G. H. Givens, Y. M. Lui, M. N. Teli, H. Zhang, W. T. Scruggs, K. W. Bowyer, P. J. Flynn, and S. Cheng · 2013
Earlier work this paper cites.
The 2013 face recognition evaluation in mobile environment
M. Günther, A. Costa-Pazo, C. Ding, E. Boutellaa, G. Chiachia, H. Zhang, M. de Assis Angeloni, V. Struc, E. Khoury, E. Vazquez-Fernandez, D. Tao, M. Bengherabi, D. Cox, S. Kiranyaz, T. de Freitas Pereira, J. Zganec-Gros, E. Argones-Rúa, N. Pinto, M. Gabbouj, F. Simões, S. Dobrisek, D. González-Jiménez, A. Rocha, M. Uliani Neto, N. Pavesic, A. Falcão, R. Violato, and S. Marcel · 2013
Earlier work this paper cites.
A case study on unconstrained facial recognition using the boston marathon bombings suspects
J. C. Klontz and A. K. Jain · 2013
Earlier work this paper cites.
Large scale unconstrained open set face database
A. Sapkota and T. E. Boult · 2013
Cited alongside, same era.
Learning face representation from scratch
D. Yi, Z. Lei, S. Liao, and S. Z. Li · 2014
Cited alongside, same era.
Report on the FG 2015 video person recognition evaluation
J. R. Beveridge, H. Zhang, B. A. Draper, P. J. Flynn, Z. Feng, P. Huber, J. Kittler, Z. Huang, S. Li, Y. Li, V. Štruc, J. Križaj, et al · 2015
Cited alongside, same era.
Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus benchmark A
B. F. Klare, B. Klein, E. Taborsky, A. Blanton, J. Cheney, K. Allen, P. Grother, A. Mah, and A. K. Jain · 2015
Cited alongside, same era.
Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Low-resolution convolutional neural networks for video face recognition
C. Herrmann, D. Willersinn, and J. Beyerer · 2016
Later among the works it cites.
Multi-person pose estimation with local joint-to-person associations
U. Iqbal and J. Gall · 2016
Later among the works it cites.
The MegaFace benchmark: 1 million faces for recognition at scale
I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, and E. Brossard · 2016
Later among the works it cites.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Later among the works it cites.
Automated border control e-gates and facial recognition systems
J. Sanchez del Rio, D. Moctezuma, C. Conde, I. Martin de Diego, and E. Cabello · 2016
Later among the works it cites.
WIDER FACE: A face detection benchmark
S. Yang, P. Luo, C.-C. Loy, and X. Tang · 2016
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J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Deep face recognition
O. M. Parkhi, A. Vedaldi, and A. Zisserman · 2015
Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
UMDFaces: An annotated face dataset for training deep networks
A. Bansal, A. Nanduri, R. Ranjan, C. D. Castillo, and R. Chellappa · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Low-quality video face recognition with deep networks and polygonal chain distance
C. Herrmann, D. Willersinn, and J. Beyerer · 2016
Cited alongside, same era.
Later among the works it cites.
Joint face detection and alignment using multitask cascaded convolutional networks
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao · 2016
Later among the works it cites.
Realtime multi-person 2D pose estimation using part affinity fields
Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh · 2017
Closest in time.
Toward open-set face recognition
M. Günther, S. Cruz, E. M. Rudd, and T. E. Boult · 2017
Closest in time.
Finding tiny faces
P. Hu and D. Ramanan · 2017
Closest in time.
The extreme value machine
E. M. Rudd, L. P. Jain, W. J. Scheirer, and T. E. Boult · 2017
Closest in time.