Problem
Research questions and friction points this paper is trying to address.
face recognition
demographic reliability
uncontrolled visual conditions
race/color bias
benchmark
UFPR-PEs: A Brazilian Face Recognition Benchmark with Self-Declared Race/Color Labels
🤖 AI Summary
为解决面部识别系统在不受控视觉条件下的人口统计学可靠性和鲁棒性问题，本文构建了UFPR-PEs基准数据集，采用巴西政治家的公开视频并标注自报种族/肤色类别进行评估。
📝 Abstract
While face recognition systems are widely deployed, ensuring their demographic reliability and robustness under uncontrolled visual conditions remains a critical challenge. To bridge this gap, we present UFPR-PEs, a benchmark for face recognition bias evaluation using public videos of elected Brazilian politicians annotated with official self-declared race/color categories. The dataset adopts the Brazilian census taxonomy, including the parda category, which has no direct equivalent in the U.S.- or Europe-centric schemas commonly used in prior benchmarks. Our benchmark is built from compressed public video and preserves difficult samples so that performance can be analyzed under realistic conditions. We describe the construction pipeline, report dataset statistics, and evaluate face recognition performance across verification and (closed- and open-set) identification settings, including subgroup analysis by race/color and difficulty level. The results show that recognition performance varies substantially with image quality, and that subgroup gaps must be interpreted jointly with visual difficulty rather than in isolation. Overall, UFPR-PEs provides a reproducible and demographically grounded setting for studying face recognition bias under challenging public video conditions.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Face Recognition
Demographic Bias
Brazilian Census Taxonomy
Public Videos
Subgroup Analysis
🔎 Similar Papers
No similar papers found.
