DiveFace

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DiveFace is a photographic facial recognition dataset comprises photographs of 24,000 people, with an average 5.5 images per person, for a total 139,677 images.Β 

Published in 2019, DiveFace was created by combining the Megaface dataset with additional annotations in order to provide a useful basis for training unbiased and 'discrimination-aware' facial recognition algorithms.

According to the authors, 'DiveFace contains annotations equally distributed among six classes related to gender and ethnicity (male, female and three ethnic groups).'Β 

The dataset broadly categorises people as: East Asian, Sub-Saharan and South Indian, and Caucasian.

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.

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Dataset πŸ€–


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Transparency and accountability πŸ™ˆ

The DiveFace dataset suffers from multiple transparency limitations:

Risks and harms πŸ›‘

With over 5,000 ethnic groups worldwide, the decision to group all people means the DiveFace dataset is also regarded as highly simplistic and likely to suffer from its own biases, with certain ethnic groups or gender identities overrepresented or underrepresented.

Investigations, assessments, audits πŸ‘οΈ

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Type: Data
Published: April 2024
Last updated: October 2024