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20162026
most citedStrengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations

217 citations · 467 across the 20 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.CV2022

Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition

Klemen Grm, Berk Kemal Özata, Vitomir Štruc +1

In this paper, we aim to address the large domain gap between high-resolution face images, e.g., from professional portrait photography, and low-quality surveillance images, e.g.,…

cs.CV2022

Assessing Bias in Face Image Quality Assessment

Žiga Babnik, Vitomir Štruc

Face image quality assessment (FIQA) attempts to improve face recognition (FR) performance by providing additional information about sample quality. Because FIQA methods attempt to…

cs.CV2022

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

Fevziye Irem Eyiokur, Alperen Kantarcı, Mustafa Ekrem Erakın +8

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced ar…

cs.CV2022★ 1 cited

Fairness in Face Presentation Attack Detection

Meiling Fang, Wufei Yang, Arjan Kuijper +2

Face recognition (FR) algorithms have been proven to exhibit discriminatory behaviors against certain demographic and non-demographic groups, raising ethical and legal concerns reg…

cs.CV2022

Iterative Optimization of Pseudo Ground-Truth Face Image Quality Labels

Žiga Babnik, Vitomir Štruc

While recent face recognition (FR) systems achieve excellent results in many deployment scenarios, their performance in challenging real-world settings is still under question. For…