Tackling Face Verification Edge Cases: In-Depth Analysis and Human-Machine Fusion Approach

Martin Knoche, Gerhard Rigoll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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 1.

Original languageEnglish
Title of host publicationProceedings of MVA 2023 - 18th International Conference on Machine Vision and Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9784885523434
DOIs
StatePublished - 2023
Event18th International Conference on Machine Vision and Applications, MVA 2023 - Hamamatsu, Japan
Duration: 23 Jul 202325 Jul 2023

Publication series

NameProceedings of MVA 2023 - 18th International Conference on Machine Vision and Applications

Conference

Conference18th International Conference on Machine Vision and Applications, MVA 2023
Country/TerritoryJapan
CityHamamatsu
Period23/07/2325/07/23

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