FaceFinder: A machine learning tool for identification of facial images from heterogenous datasets
FaceFinder is capable of sorting through a heterogenous dataset of photos of patients with craniofacial disease and identifying high-quality front-facing facial images. This capability allows for automated sorting of large databases that can facilitate and expedite data preparation for further downstream analyses involving artificial intelligence and facial landmarking.
Authors
- George R. Nahass (ORCID: https://orcid.org/0000-0001-9395-0862)
- Akriti Choudhary (ORCID: https://orcid.org/0000-0002-5680-7327)
- Kevin Heinze
- Chad A. Purnell (ORCID: https://orcid.org/0000-0001-8540-6269)
- Pete Setabutr
- Ann Q. Tran (ORCID: https://orcid.org/0000-0002-3176-5909)
- Jeffrey Peterson (ORCID: https://orcid.org/0000-0001-7059-1131)
- Nikhila Khandwala
Institutions
- New York Eye and Ear Infirmary (US)
- University of Illinois Chicago (US)
Publication Details
- Journal
- AJO International
- Published
- 2024-11-07
- DOI
- https://doi.org/10.1016/j.ajoint.2024.100083
- Primary Topic
- Face recognition and analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00