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

arXiv: https://arxiv.org/abs/2304.08134 · HTML: https://mlsanity.com/paper/2304.08134/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.07698/) (2018)
- [Face Transformer for Recognition](https://mlsanity.com/paper/2103.14803/) (2021)
- [Pi-nets: Deep Polynomial Neural Networks](https://mlsanity.com/paper/2003.03828/) (2020)
- [Cross-Quality LFW: A Database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/2108.10290/) (2021)
- [Explainable Model-Agnostic Similarity and Confidence in Face Verification](https://mlsanity.com/paper/2211.13735/) (2022)
- [MLFW: A Database for Face Recognition on Masked Faces](https://mlsanity.com/paper/2109.05804/) (2021)
- [Octuplet Loss: Make Face Recognition Robust to Image Resolution](https://mlsanity.com/paper/2207.06726/) (2022)
- [Susceptibility to Image Resolution in Face Recognition and Trainings Strategies](https://mlsanity.com/paper/2107.03769/) (2021)

## Built on by (most cited first, 1 of 1)

- [Effective Generative AI: The Human-Algorithm Centaur](https://mlsanity.com/paper/2406.10942/) (2024)

## Abstract (arXiv)

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.