Fetching the paper…
Reading the bibliography…
This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results.
Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Conference on Computer Vision and Pattern Recognition (CVPR). vol. 1, pp. 539–546 (2005)
2005
Earlier work this paper cites.
Pearl, J.: Causal inference in statistics: An overview. Statistics Surveys 3
2009
Earlier work this paper cites.
2011
Earlier work this paper cites.
Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1521–1528 (2011)
2011
Earlier work this paper cites.
Facial recognition tech under spotlight after boston bombings. Biometric Technology Today 2013
2013
Earlier work this paper cites.
Bino, S., Bernerd, F.: Variations in skin colour and the biological consequences of ultraviolet radiation exposure. British Journal of Dermatology 169
2013
Earlier work this paper cites.
Rothe, R., Timofte, R., Gool, L.V.: DEX: Deep expectation of apparent age from a single image. In: International Conference on Computer Vision Workshops (ICCVW). pp. 252–257 (2015)
2015
Earlier work this paper cites.
Dieterich, W., Mendoza, C., Brennan, T.: Compas risk scales : Demonstrating accuracy equity and predictive parity performance of the compas risk scales in broward county. [online] Available at: https://go.volarisgroup.com/rs/430-MBX-989/images/ProPublica_Commentary_Final_070616.pdf
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770–778 (2016)
2016
Earlier work this paper cites.
Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.: The megaface benchmark: 1 million faces for recognition at scale. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4873–4882 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Learned-Miller, E., Huang, G.B., RoyChowdhury, A., Li, H., Hua, G.: Labeled Faces in the Wild: A Survey, pp. 189–248. Springer Publishing Company, Incorporated, 1st edn. (2016), Advances in Face Detection and Facial Image Analysis
2016
Earlier work this paper cites.
ProPublica, by Julia Angwin, Larson, J., Mattu, S., Kirchner, L.: Machine bias: There’s software used across the country to predict future criminals and it’s biased against blacks. [online] Available at: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Chouldechova, A.: Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data 5
2017
Earlier work this paper cites.
Kusner, M.J., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4066–4076. Curran Associates, Inc. (2017)
2017
Earlier work this paper cites.
Nabi, R., Shpitser, I.: Fair inference on outcomes. CoRR abs/1705.10378
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
American Civil Liberties Union, by Jacob Snow: Amazon’s Face Recognition Falsely Matched 28 Members of Congress With Mugshots. [online] Available at: https://www.aclu.org/blog/privacy-technology/surveillance-technologies/amazons-face-recognition-falsely-matched-28
2018
Earlier work this paper cites.
Anne Hendricks, L., Burns, K., Saenko, K., Darrell, T., Rohrbach, A.: Women also snowboard: Overcoming bias in captioning models. In: European Conference on Computer Vision (ECCV). pp. 793–811 (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Berk, R., Heidari, H., Jabbari, S., Kearns, M., Roth, A.: Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research (2018)
2018
Earlier work this paper cites.
Buolamwini, J., Gebru, T.: Gender shades: Intersectional accuracy disparities in commercial gender classification. In: Proceedings of the 1st Conference on Fairness, Accountability and Transparency. Proceedings of Machine Learning Research, vol. 81, pp. 77–91. PMLR (2018)
2018
Earlier work this paper cites.
Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: VGGFace2: A dataset for recognising faces across pose and age. In: International Conference on Automatic Face Gesture Recognition (FG). pp. 67–74 (2018)
2018
Cited alongside, same era.
Davies, B., Innes, M., Dawson, A.: An Evaluation of South Wales Police’s Use of Automated Facial Recognition. [online] Available at: https://static1.squarespace.com/static/51b06364e4b02de2f57fd72e/t/5bfd4fbc21c67c2cdd692fa8/1543327693640/AFR+Report+%5BDigital%5D.pdf
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Maze, B., Adams, J., Duncan, J.A., Kalka, N., Miller, T., Otto, C., Jain, A.K., Niggel, W.T., Anderson, J., Cheney, J., Grother, P.: IARPA Janus Benchmark - C: Face dataset and protocol. In: International Conference on Biometrics (ICB). pp. 158–165 (2018)
2018
Wang, T., Zhao, J., Yatskar, M., Chang, K.W., Ordonez, V.: Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations. In: International Conference on Computer Vision (ICCV). pp. 5310–5319 (2019)
2019
Later among the works it cites.
Albiero, V., Bowyer, K.W., Vangara, K., King, M.C.: Does face recognition accuracy get better with age? Deep face matchers say no. Winter Conference on Applications of Computer Vision (WACV) pp. 250–258 (2020)
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Rothe, R., Timofte, R., Gool, L.V.: Deep expectation of real and apparent age from a single image without facial landmarks. International Journal of Computer Vision 126
2018
Cited alongside, same era.
The Guardian, by Wang Xueying: China testing facial-recognition surveillance system in Xinjiang - report. [online] Available at: https://www.theguardian.com/world/2018/jan/18/china-testing-facial-recognition-surveillance-system-in-xinjiang-report
2018
Cited alongside, same era.
The New York Times, by Paul Mozur: Inside China’s Dystopian Dreams: A.I., Shame and Lots of Cameras. [online] Available at: https://www.nytimes.com/2018/07/08/business/china-surveillance-technology.html
2018
Cited alongside, same era.
Verma, S., Rubin, J.: Fairness definitions explained. In: Proceedings of the International Workshop on Software Fairness. pp. 1–7 (2018)
2018
Cited alongside, same era.
Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., Liu, W.: CosFace: Large margin cosine loss for deep face recognition. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Bird, S., Hutchinson, B., Kenthapadi, K., Kıcıman, E., Mitchell, M.: Fairness-aware machine learning: Practical challenges and lessons learned. In: Companion Proceedings of The 2019 World Wide Web Conference. p. 1297–1298 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Escalante, H.J., Kaya, H., Salah, A., Escalera, S., Güçlütürk, Y., Güçlü, U., Baró, X., Guyon, I., Jacques Junior, J.C.S., Madadi, M., Ayache, S., Viegas, E., Gurpinar, F., Wicaksana, A.S., Liem, C., Van Gerven, M.A.J., Van Lier, R.: Modeling, recognizing, and explaining apparent personality from videos. IEEE Transactions on Affective Computing (2020)
2020
Closest in time.
2020
Closest in time.
Jayaraman, U., Gupta, P., Gupta, S., Arora, G., Tiwari, K.: Recent development in face recognition. Neurocomputing 408
2020
Closest in time.
Lo Piano, S.: Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward. Humanities and Social Sciences Communications 7
2020
Closest in time.
Pessach, D., Shmueli, E.: Algorithmic fairness. CoRR abs/2001.09784
2020
Closest in time.
Pierce, J., Wong, R.Y., Merrill, N.: Sensor illumination: Exploring design qualities and ethical implications of smart cameras and image/video analytics. In: Conference on Human Factors in Computing Systems. p. 1–19 (2020)
2020
Closest in time.
Raji, I.D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., Denton, E.: Saving face: Investigating the ethical concerns of facial recognition auditing. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. p. 145–151 (2020)
2020
Closest in time.
Robinson, J.P., Livitz, G., Henon, Y., Qin, C., Fu, Y., Timoner, S.: Face recognition: Too bias, or not too bias? In: Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). pp. 1–10 (2020)
2020
Closest in time.
2020
Closest in time.
The New York Times, by Jennifer Valentino-DeVries: How the Police Use Facial Recognition, and Where It Falls Short. [online] Available at: https://www.nytimes.com/2020/01/12/technology/facial-recognition-police.html
2020
Closest in time.
The Washington Post, by Jay Greene: Microsoft won’t sell police its facial-recognition technology, following similar moves by Amazon and IBM. [online] Available at: https://www.washingtonpost.com/technology/2020/06/11/microsoft-facial-recognition
2020
Closest in time.
THINKPolicy Blog, by Arvind Krishna: IBM CEO’s Letter to Congress on Racial Justice Reform. [online] Available at: https://www.ibm.com/blogs/policy/facial-recognition-sunset-racial-justice-reforms
2020
Closest in time.
US Day One Blog: We are implementing a one-year moratorium on police use of rekognition. [online] Available at: https://blog.aboutamazon.com/policy/we-are-implementing-a-one-year-moratorium-on-police-use-of-rekognition
2020
Closest in time.
Vowels, M.J., Camgoz, N.C., Bowden, R.: NestedVAE: Isolating common factors via weak supervision. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9202–9212 (2020)
2020
Closest in time.
Wang, M., Deng, W.: Mitigating bias in face recognition using skewness-aware reinforcement learning. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9322–9331 (2020)
2020
Closest in time.
Wang, Z., Qinami, K., Karakozis, I.C., Genova, K., Nair, P., Hata, K., Russakovsky, O.: Towards fairness in visual recognition: Effective strategies for bias mitigation. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8919–8928 (2020)
2020
Closest in time.
Yu, J., Hao, X., Xie, H., Yu, Y.: Fair face recognition using data balancing, enhancement and fusion. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (ECCVW), in press
2020
Closest in time.
Yucer, S., Akcay, S., Al-Moubayed, N., Breckon, T.P.: Exploring racial bias within face recognition via per-subject adversarially-enabled data augmentation. In: Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2020)
2020
Closest in time.
Zhou, S.: AsArcFace: Asymmetric additive angular margin loss for fairface recognition. In: Proceedings of the European Conference on Computer Vision Workshops (ECCVW), in press
2020
Closest in time.