Year: 2023
Type: conference-paper
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 GitHub1.
Authors: Martin Knoche, Gerhard Rigoll
Institution: Technical University of Munich
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4386072500