HRT Transgender Dataset
HRT Transgender Dataset
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Created in 2013, the HRT Transgender Dataset helps facial recognition systems to identify users of Hormone Replacement Therapy (HRT) transitioning or who have already transitioned from one gender to another.
The dataset comprised 10,000 images of 38 people, with an average of 278 images per subject taken from publicly available YouTube videos 'under real-world conditions, including variations in pose, illumination, expression, and occlusion'.ย
Facial recognition system
A facial recognition system is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces.
Source: Wikipedia ๐
Dataset ๐ค
Dataset ๐ค
Website
Released: 2013
Avai: Withdrawn
Developer: University of North Carolina, Wilmington (UNCW)
Purpose: Train facial recognition systems; Identify HRT users
Type: Database/dataset
Technique: Computer vision; Facial recognition
Transparency and accountability ๐
Transparency and accountability ๐
The HRT Transgender dataset is seen to have multiple transparency limitations:
Limited access to raw data. Researchers attempting to audit the database have faced difficulties in accessing the original, unprocessed data.
Lack of clear documentation. There appears to be insufficient documentation regarding the data collection methods, inclusion criteria, and data processing procedures used to compile the database.
Potential selection bias. The database may not be representative of the entire transgender population, as it likely only includes individuals who have disclosed their transgender identity to healthcare providers and had it recorded in their medical records.
Risks and harms ๐
Risks and harms ๐
The HRT Transgender dataset has been criticised for using and exposing the data of YouTube users without consent and for causing potential bias against the LGBTQ community.
Incidents and issues ๐ฅ
Incidents and issues ๐ฅ
Research, advocacy ๐งฎ
Research, advocacy ๐งฎ
Keyes, O., Austin, J. (2022). Feeling fixes: Mess and emotion in algorithmic audits
Scheuerman M.K., Pape M., Hanna A. (2021). Auto-essentialization: Gender in automated facial analysis as extended colonial project (pdf)
Kumar V., Ramachandra R., Namboodiri A.M., Busch C. (2016). Robust transgender face recognition: Approach based on appearance and therapy factors
Page info
Type: Data
Published: January 2023
Last updated: October 2024
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