# Demographic Fairness in Face Identification: The Watchlist Imbalance Effect

arXiv: https://arxiv.org/abs/2106.08049 · HTML: https://mlsanity.com/paper/2106.08049/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Demographic Bias in Biometrics: A Survey on an Emerging Challenge](https://mlsanity.com/paper/2003.02488/) (2020)
- [FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age](https://mlsanity.com/paper/1908.04913/) (2019)
- [Analysis of Gender Inequality In Face Recognition Accuracy](https://mlsanity.com/paper/2002.00065/) (2020)
- [Fairness in Biometrics: a figure of merit to assess biometric verification systems](https://mlsanity.com/paper/2011.02395/) (2020)
- [Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are](https://mlsanity.com/paper/2008.06989/) (2020)
- [Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometrics](https://mlsanity.com/paper/1912.01842/) (2019)

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

- [Fairness on Synthetic Visual and Thermal Mask Images](https://mlsanity.com/paper/2209.08762/) (2022)

## Abstract (arXiv)

Recently, different researchers have found that the gallery composition of a face database can induce performance differentials to facial identification systems in which a probe image is compared against up to all stored reference images to reach a biometric decision. This negative effect is referred to as "watchlist imbalance effect". In this work, we present a method to theoretically estimate said effect for a biometric identification system given its verification performance across demographic groups and the composition of the used gallery. Further, we report results for identification experiments on differently composed demographic subsets, i.e. females and males, of the public academic MORPH database using the open-source ArcFace face recognition system. It is shown that the database composition has a huge impact on performance differentials in biometric identification systems, even if performance differentials are less pronounced in the verification scenario. This study represents the first detailed analysis of the watchlist imbalance effect which is expected to be of high interest for future research in the field of facial recognition.