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Deep learning face recognition models are used by state-of-the-art surveillance systems to identify individuals passing through public areas (e.g., airports).
Advhat: Real-world adversarial attack on arcface face id system
Komkov, S.; and Petiushko, A. 2019 · 1908
Earlier work this paper cites.
Measuring colorfulness in natural images
Hasler, D.; and Suesstrunk, S. E. 2003 · 2003
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Robust real-time face detection
Viola, P.; and Jones, M. J. 2004 · 2004
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Can facial cosmetics affect the matching accuracy of face recognition systems?
Dantcheva, A.; Chen, C.; and Ross, A. 2012 · 2012
Earlier work this paper cites.
Face recognition methods & applications
Parmar, D. N.; and Mehta, B. B. 2014 · 2014
Earlier work this paper cites.
Deep learning face representation by joint identification-verification
Sun, Y.; Wang, X.; and Tang, X. 2014 · 2014
Earlier work this paper cites.
Learning face representation from scratch
Yi, D.; Lei, Z.; Liao, S.; and Li, S. Z. 2014 · 2014
Earlier work this paper cites.
A convolutional neural network cascade for face detection
Li, H.; Lin, Z.; Shen, X.; Brandt, J.; and Hua, G. 2015 · 2015
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Deep face recognition
Parkhi, O. M.; Vedaldi, A.; and Zisserman, A. 2015 · 2015
Earlier work this paper cites.
Web-based online embedded door access control and home security system based on face recognition
Sahani, M.; Nanda, C.; Sahu, A. K.; and Pattnaik, B. 2015 · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
Schroff, F.; Kalenichenko, D.; and Philbin, J. 2015 · 2015
Earlier work this paper cites.
From facial parts responses to face detection: A deep learning approach
Yang, S.; Luo, P.; Loy, C.-C.; and Tang, X. 2015 · 2015
Earlier work this paper cites.
Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Guo, Y.; Zhang, L.; Hu, Y.; He, X.; and Gao, J. 2016 · 2016
Cited alongside, same era.
Labeled faces in the wild: A survey
Learned-Miller, E.; Huang, G. B.; RoyChowdhury, A.; Li, H.; and Hua, G. 2016 · 2016
Cited alongside, same era.
Large-margin softmax loss for convolutional neural networks
Liu, W.; Wen, Y.; Yu, Z.; and Yang, M. 2016 · 2016
Cited alongside, same era.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Sharif, M.; Bhagavatula, S.; Bauer, L.; and Reiter, M. K. 2016 · 2016
Cited alongside, same era.
Joint face detection and alignment using multitask cascaded convolutional networks
Zhang, K.; Zhang, Z.; Li, Z.; and Qiao, Y. 2016 · 2016
Cited alongside, same era.
Spoofing faces using makeup: An investigative study
Chen, C.; Dantcheva, A.; Swearingen, T.; and Ross, A. 2017 · 2017
Evading face recognition via partial tampering of faces
Majumdar, P.; Agarwal, A.; Singh, R.; and Vatsa, M. 2019 · 2019
Later among the works it cites.
Impact and detection of facial beautification in face recognition: An overview
Rathgeb, C.; Dantcheva, A.; and Busch, C. 2019 · 2019
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A general framework for adversarial examples with objectives
Sharif, M.; Bhagavatula, S.; Bauer, L.; and Reiter, M. K. 2019 · 2019
Later among the works it cites.
Fooling automated surveillance cameras: adversarial patches to attack person detection
Thys, S.; Van Ranst, W.; and Goedemé, T. 2019 · 2019
Later among the works it cites.
Generating adversarial examples by makeup attacks on face recognition
Zhu, Z.-A.; Lu, Y.-Z.; and Chiang, C.-K. 2019 · 2019
Later among the works it cites.
Advfaces: Adversarial face synthesis
Deb, D.; Zhang, J.; and Jain, A. K. 2019 · 2020
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Cited alongside, same era.
Sphereface: Deep hypersphere embedding for face recognition
Liu, W.; Wen, Y.; Yu, Z.; Li, M.; Raj, B.; and Song, L. 2017 · 2017
Cited alongside, same era.
Design of face detection and recognition system for smart home security application
Wati, D. A. R.; and Abadianto, D. 2017 · 2017
Cited alongside, same era.
Threat of adversarial attacks on deep learning in computer vision: A survey
Akhtar, N.; and Mian, A. 2018 · 2018
Cited alongside, same era.
Arcface: Additive angular margin loss for deep face recognition
Deng, J.; Guo, J.; Xue, N.; and Zafeiriou, S. 2019 · 2019
Cited alongside, same era.
Efficient decision-based black-box adversarial attacks on face recognition
Dong, Y.; Su, H.; Wu, B.; Li, Z.; Liu, W.; Zhang, T.; and Zhu, J. 2019 · 2019
Cited alongside, same era.
Intriguing properties of neural networks
Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; and Fergus, R. 2013a
Cited in the paper.
Later among the works it cites.
Amora: Black-box adversarial morphing attack
Wang, R.; Juefei-Xu, F.; Guo, Q.; Huang, Y.; Xie, X.; Ma, L.; and Liu, Y. 2020 · 2020
Later among the works it cites.
A review of state-of-the-art in Face Presentation Attack Detection: From early development to advanced deep learning and multi-modal fusion methods
Abdullakutty, F.; Elyan, E.; and Johnston, P. 2021 · 2021
Closest in time.
Makeup Presentation Attack Potential Revisited: Skills Pay the Bills
Drozdowski, P.; Grobarek, S.; Schurse, J.; Rathgeb, C.; Stockhardt, F.; and Busch, C. 2021 · 2021
Closest in time.
Attacks on state-of-the-art face recognition using attentional adversarial attack generative network
Yang, L.; Song, Q.; and Wu, Y. 2021 · 2021
Closest in time.
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition
Yin, B.; Wang, W.; Yao, T.; Guo, J.; Kong, Z.; Ding, S.; Li, J.; and Liu, C. 2021 · 2021
Closest in time.