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Using the face as a biometric identity trait is motivated by the contactless nature of the capture process and the high accuracy of the recognition algorithms.
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2017
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J. Yang, L. Luo, J. Qian, Y. Tai, F. Zhang, and Y. Xu, “Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes,”
2017
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O. Arandjelović, “Reimagining the central challenge of face recognition: Turning a problem into an advantage,”
2018
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S. Chen, Y. Liu, X. Gao, and Z. Han, “Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices,” in
2018
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2018
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M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in
2018
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——, “Ongoing face recognition vendor test (frvt) part 6b: Face recognition accuracy with face masks using post-covid-19 algorithms,” Tech. Rep., 2020-11-30 2020
2020
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A. Anwar and A. Raychowdhury, “Masked face recognition for secure authentication,” 2020
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B. Qin and D. Li, “Identifying facemask-wearing condition using image super-resolution with classification network to prevent covid-19,”
2020
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2020
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Y. Li, K. Guo, Y. Lu, and L. Liu, “Cropping and attention based approach for masked face recognition,”
2021
Closest in time.
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B. Maze, J. C. Adams, J. A. Duncan, N. D. Kalka, T. Miller, C. Otto, A. K. Jain, W. T. Niggel, J. Anderson, J. Cheney, and P. Grother, “IARPA janus benchmark - C: face dataset and protocol,” in
2018
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2018
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J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in
2019
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L. Song, D. Gong, Z. Li, C. Liu, and W. Liu, “Occlusion robust face recognition based on mask learning with pairwise differential siamese network,” in
2019
Cited alongside, same era.
N. Damer, J. H. Grebe, C. Chen, F. Boutros, F. Kirchbuchner, and A. Kuijper, “The effect of wearing a mask on face recognition performance: an exploratory study,” in
2020
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M. Ngan, P. Grother, and K. Hanaoka, “Ongoing face recognition vendor test (frvt) part 6a: Face recognition accuracy with masks using pre- covid-19 algorithms,” Tech. Rep., 2020-07-24 2020
2020
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M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, “A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the covid-19 pandemic,”
2021
Closest in time.
N. Damer, F. Boutros, M. Süßmilch, F. Kirchbuchner, and A. Kuijper, “Extended evaluation of the effect of real and simulated masks on face recognition performance,”
2021
Closest in time.
Neurotechnology. (2021) Neurotechnology: Fingerprint, face, eye iris, voice and palm print identification, speaker and object recognition software
2021
Closest in time.
Cognitec. (2021) Cognitec: The face recognition company
2021
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G. Jeevan, G. C. Zacharias, M. S. Nair, and J. Rajan, “An empirical study of the impact of masks on face recognition,”
2022
Closest in time.
Department of Homeland Security, “Biometric Technology Rally at MDTF,”
2026
Closest in time.