Goldilocks Test Sets for Face Verification
Haiyu Wu, Sicong Tian, Aman Bhatta, Jacob Gutierrez, Grace Bezold, Genesis Argueta, Karl Ricanek Jr., Michael C. King, Kevin W. Bowyer

TL;DR
This paper introduces challenging, high-quality face verification test sets that reveal weaknesses in current models by focusing on attribute variation and similar-looking identities, avoiding artificial image degradation.
Contribution
The authors propose three new face verification test sets addressing attribute differences and similar-looking persons, with rigorous assembly rules for fair evaluation.
Findings
Test sets are more challenging than existing ones without image quality reduction.
Models show significant performance drops on the new test sets.
Proposed datasets reveal overlooked weaknesses in face verification algorithms.
Abstract
Reported face verification accuracy has reached a plateau on current well-known test sets. As a result, some difficult test sets have been assembled by reducing the image quality or adding artifacts to the image. However, we argue that test sets can be challenging without artificially reducing the image quality because the face recognition (FR) models suffer from correctly recognizing 1) the pairs from the same identity (i.e., genuine pairs) with a large face attribute difference, 2) the pairs from different identities (i.e., impostor pairs) with a small face attribute difference, and 3) the pairs of similar-looking identities (e.g., twins and relatives). We propose three challenging test sets to reveal important but ignored weaknesses of the existing FR algorithms. To challenge models on variation of facial attributes, we propose Hadrian and Eclipse to address facial hair differences…
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Taxonomy
TopicsDeception detection and forensic psychology
