Tackling Face Verification Edge Cases: In-Depth Analysis and Human-Machine Fusion Approach
This work addresses edge cases in face verification systems, which is an incremental improvement for applications requiring high accuracy.
The paper tackled face verification edge cases by analyzing challenging settings for state-of-the-art models and conducting a human study with 60 participants, demonstrating that combining machine and human decisions improves performance on various benchmark datasets.
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.