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Facial attributes are soft-biometrics that allow limiting the search space, e.g., by rejecting identities with non-matching facial characteristics such as nose sizes or eyebrow shapes.
Learning algorithms for classification: A comparison on handwritten digit recognition
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N. Kumar, P. Belhumeur, and S. Nayar · 2008
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Describable Visual Attributes for Face Verification and Image Search
N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar · 2011
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A. Maronidis, D. Bolis, A. Tefas, and I. Pitas · 2011
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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
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Multi-attribute spaces: Calibration for attribute fusion and similarity search
W. J. Scheirer, N. Kumar, P. N. Belhumeur, and T. E. Boult · 2012
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Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Some improvements on deep convolutional neural network based image classification
A. G. Howard · 2014
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Caffe: Convolutional architecture for fast feature embedding
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Transformation pursuit for image classification
M. Paulin, J. Revaud, Z. Harchaoui, F. Perronnin, and C. Schmid · 2014
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, M. Burge, and A. K. Jain · 2015
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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Deep face recognition
O. M. Parkhi, A. Vedaldi, and A. Zisserman · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Do we really need to collect millions of faces for effective face recognition?
I. Masi, A. Tran, T. Hassner, J. T. Leksut, and G. Medioni · 2016
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Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
Cited alongside, same era.
Facial landmark detection by deep multi-task learning
Z. Zhang, P. Luo, C. C. Loy, and X. Tang · 2014
Cited alongside, same era.
The virtues of peer pressure: A simple method for discovering high-value mistakes
S. Baluja, M. Covell, and R. Sukthankar · 2015
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Impact of eye detection error on face recognition performance
A. Dutta, M. Günther, L. El Shafey, S. Marcel, R. Veldhuis, and L. Spreeuwers · 2015
Cited alongside, same era.
Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
Cited alongside, same era.
Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition, 2016
R. Ranjan, V. M. Patel, and R. Chellappa · 2016
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Are facial attributes adversarially robust?
A. Rozsa, M. Günther, E. M. Rudd, and T. E. Boult · 2016
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Adversarial diversity and hard positive generation
A. Rozsa, E. M. Rudd, and T. E. Boult · 2016
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MOON: A mixed objective optimization network for the recognition of facial attributes
E. M. Rudd, M. Günther, and T. E. Boult · 2016
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Walk and learn: Facial attribute representation learning from egocentric video and contextual data
J. Wang, Y. Cheng, and R. S. Feris · 2016
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