Robinson, Joseph P., Can Qin, Yann Henon, Samson Timoner, Yun Fu

This project investigates bias in automatic facial recognition (FR). Specifically, subjects are grouped into predefined subgroups based on gender, ethnicity, and age. We propose a novel image collection called Balanced Faces in the Wild (BFW), which is balanced across eight subgroups (i.e., 800 face images of 100 subjects, each with 25 face samples).

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[1] Joseph Robinson, "Balanced Faces in the Wild", IEEE Dataport, 2022. [Online]. Available: http://dx.doi.org/10.21227/nmsj-df12. Accessed: Jan. 15, 2025.
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doi = {10.21227/nmsj-df12},
url = {http://dx.doi.org/10.21227/nmsj-df12},
author = {Joseph Robinson },
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title = {Balanced Faces in the Wild},
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T1 - Balanced Faces in the Wild
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Joseph Robinson. (2022). Balanced Faces in the Wild. IEEE Dataport. http://dx.doi.org/10.21227/nmsj-df12
Joseph Robinson, 2022. Balanced Faces in the Wild. Available at: http://dx.doi.org/10.21227/nmsj-df12.
Joseph Robinson. (2022). "Balanced Faces in the Wild." Web.
1. Joseph Robinson. Balanced Faces in the Wild [Internet]. IEEE Dataport; 2022. Available from : http://dx.doi.org/10.21227/nmsj-df12
Joseph Robinson. "Balanced Faces in the Wild." doi: 10.21227/nmsj-df12