# Generalizability and Application of the Skin Reflectance Estimate Based on Dichromatic Separation (SREDS)

arXiv: https://arxiv.org/abs/2309.01235 · HTML: https://mlsanity.com/paper/2309.01235/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 3 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [FairGAN: Fairness-aware Generative Adversarial Networks](https://mlsanity.com/paper/1805.11202/) (2018)
- [Meta Balanced Network for Fair Face Recognition](https://mlsanity.com/paper/2205.06548/) (2022)
- [SensitiveLoss: Improving Accuracy and Fairness of Face Representations with Discrimination-Aware Deep Learning](https://mlsanity.com/paper/2004.11246/) (2020)
- [Fairness in Biometrics: a figure of merit to assess biometric verification systems](https://mlsanity.com/paper/2011.02395/) (2020)
- [Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization](https://mlsanity.com/paper/2002.03592/) (2020)
- [Beyond Identity: What Information Is Stored in Biometric Face Templates?](https://mlsanity.com/paper/2009.09918/) (2020)
- [Evaluating Proposed Fairness Models for Face Recognition Algorithms](https://mlsanity.com/paper/2203.05051/) (2022)
- [SREDS: A dichromatic separation based measure of skin color](https://mlsanity.com/paper/2104.02926/) (2021)

## Abstract (arXiv)

Face recognition (FR) systems have become widely used and readily available in recent history. However, differential performance between certain demographics has been identified within popular FR models. Skin tone differences between demographics can be one of the factors contributing to the differential performance observed in face recognition models. Skin tone metrics provide an alternative to self-reported race labels when such labels are lacking or completely not available e.g. large-scale face recognition datasets. In this work, we provide a further analysis of the generalizability of the Skin Reflectance Estimate based on Dichromatic Separation (SREDS) against other skin tone metrics and provide a use case for substituting race labels for SREDS scores in a privacy-preserving learning solution. Our findings suggest that SREDS consistently creates a skin tone metric with lower variability within each subject and SREDS values can be utilized as an alternative to the self-reported race labels at minimal drop in performance. Finally, we provide a publicly available and open-source implementation of SREDS to help the research community. Available at https://github.com/JosephDrahos/SREDS