Benchmarks on Bias Faces in the Wild
Face Recognition: Too Bias, or Not Too Bias?
Robinson, Joseph P., Gennady Livitz, Yann Henon, Can Qin, Yun Fu, and Samson Timoner. “Face recognition: too bias, or not too bias? ” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0-1. 2020.
Balanced Faces in the Wild (BFW): Data, Code, Evaluations
version: 0.4.5 (following Semantic Versioning Scheme– learn more here, https://semver.org)
Intended to address problems of bias in facial recognition, we built BFW as a labeled data resource made available for evaluating recognitiion systems on a corpus of facial imagery made-up of EQUAL face count for all subjects, which are EQUAL across demographics, and, thus, face data balanced in faces per subject, subjeccts per ethniciity, ethnicity (or faces) per gender.
Data can