Learning to classify gender from four million images
- Submitting institution
-
University of Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 94738526
- Type
- D - Journal article
- DOI
-
10.1016/j.patrec.2015.02.006
- Title of journal
- Pattern Recognition Letters
- Article number
- -
- First page
- 35
- Volume
- 58
- Issue
- -
- ISSN
- 0167-8655
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
A - Artificial Intelligence and Autonomy
- Citation count
- 38
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This study investigated methods to train machine learning algorithms to perform face-gender classification. At the time it was the largest published study of this kind, and delivered the state of the art performance. This project demonstrated the use of big-data and AI, combined with web science, to train valuable image classifiers, which were later used also in media-analysis studies. This well-cited study became part of comparisons from many following papers (e.g., Dantcheva etc. IEEE Trans. on IFS'16; Sun etc. IEEE PAMI'17).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -