# Exploring a Makeup Support System for Transgender Passing based on Automatic Gender Recognition

arXiv: https://arxiv.org/abs/2103.04544 · HTML: https://mlsanity.com/paper/2103.04544/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [Diversity in Faces](https://mlsanity.com/paper/1901.10436/) (2019)
- [On Face Segmentation, Face Swapping, and Face Perception](https://mlsanity.com/paper/1704.06729/) (2017)

## Built on by (most cited first, 2 of 2)

- [Robustness Disparities in Commercial Face Detection](https://mlsanity.com/paper/2108.12508/) (2021)
- [More than One Step at a Time: Designing Procedural Feedback for Non-visual Makeup Routines](https://mlsanity.com/paper/2507.03942/) (2025)

## Abstract (arXiv)

How to handle gender with machine learning is a controversial topic. A growing critical body of research brought attention to the numerous issues transgender communities face with the adoption of current automatic gender recognition (AGR) systems. In contrast, we explore how such technologies could potentially be appropriated to support transgender practices and needs, especially in non-Western contexts like Japan. We designed a virtual makeup probe to assist transgender individuals with passing, that is to be perceived as the gender they identify as. To understand how such an application might support expressing transgender individuals gender identity or not, we interviewed 15 individuals in Tokyo and found that in the right context and under strict conditions, AGR based systems could assist transgender passing.