Simulated Masked Face Recognition Dataset (SMFRD)
Simulated Masked Face Recognition Dataset (SMFRD)
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SMFRD (or Simulated Masked Face Recognition Dataset) is a dataset of masked faces intended to enable facial recognition systems to identify the individuals behind the masks.
Released in March 2020 by researchers at Wuhan University in China, the set is a derivative of the Labeled Faces in the Wild (LBW) dataset, with facemasks superimposed. LBW was the first dataset to use facial images scraped from websites and applications.
Released at the height of the COVID-19 pandemic, SMFRD was seen as helpful to limiting the spread of the pandemic in China and is freely available to industry and academia.
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
SMFRD dataset (Github)
Status: Active
Released: 2020
Purpose: Train facial recognition systems
Type: Database/dataset
Technique: Computer vision; Facial recognition; Machine learning
Documents ๐
Documents ๐
Transparency and accountability ๐
Transparency and accountability ๐
The Simulated Masked Face Recognition Dataset (SMFRD) is seen to suffer from multiple transparency limitations.
Lack of clear consent. It is unclear if and how consent was obtained from individuals whose images were used or simulated in the dataset.
Limited information on data generation. The exact methods used to simulate masked faces may not be fully disclosed, making it difficult to assess the dataset's representativeness and potential biases.
Inadequate documentation of intended use. The dataset's intended applications and potential misuses are not clearly outlined.
Risks and harms ๐
Risks and harms ๐
The Simulated Masked Face Recognition Dataset has raised concerns about privacy violations and its potential misuse in surveillance systems, thereby potentially limiting human rights and civil freedoms.
Incidents and issues ๐ฅ
Incidents and issues ๐ฅ
Research, advocacy ๐งฎ
Research, advocacy ๐งฎ
Peg. K., Mathur A., Narayanan A. (2021). Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers
Page info
Type: Data
Published: February 2023
Last updated: October 2024
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