499 research outputs found
Tackling Face Verification Edge Cases: In-Depth Analysis and Human-Machine Fusion Approach
Nowadays, face recognition systems surpass human performance on several
datasets. However, there are still edge cases that the machine can't correctly
classify. This paper investigates the effect of a combination of machine and
human operators in the face verification task. First, we look closer at the
edge cases for several state-of-the-art models to discover common datasets'
challenging settings. Then, we conduct a study with 60 participants on these
selected tasks with humans and provide an extensive analysis. Finally, we
demonstrate that combining machine and human decisions can further improve the
performance of state-of-the-art face verification systems on various benchmark
datasets. Code and data are publicly available on GitHub
Cross-Quality LFW: A Database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments
Real-world face recognition applications often deal with suboptimal image
quality or resolution due to different capturing conditions such as various
subject-to-camera distances, poor camera settings, or motion blur. This
characteristic has an unignorable effect on performance. Recent
cross-resolution face recognition approaches used simple, arbitrary, and
unrealistic down- and up-scaling techniques to measure robustness against
real-world edge-cases in image quality. Thus, we propose a new standardized
benchmark dataset and evaluation protocol derived from the famous Labeled Faces
in the Wild (LFW). In contrast to previous derivatives, which focus on pose,
age, similarity, and adversarial attacks, our Cross-Quality Labeled Faces in
the Wild (XQLFW) maximizes the quality difference. It contains only more
realistic synthetically degraded images when necessary. Our proposed dataset is
then used to further investigate the influence of image quality on several
state-of-the-art approaches. With XQLFW, we show that these models perform
differently in cross-quality cases, and hence, the generalizing capability is
not accurately predicted by their performance on LFW. Additionally, we report
baseline accuracy with recent deep learning models explicitly trained for
cross-resolution applications and evaluate the susceptibility to image quality.
To encourage further research in cross-resolution face recognition and incite
the assessment of image quality robustness, we publish the database and code
for evaluation.Comment: 9 pages, 4 figures, 2 table
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