Face swapping in seizure videos for patient deidentification
This is the first study evaluating AI face swapping models in epileptic seizure video clips. Optimization of AI face-swapping models could enhance the accessibility of seizure videos for education and research while protecting patient privacy and maintaining semiology.
Authors
- Aileen McGonigal (ORCID: https://orcid.org/0000-0001-6775-5318)
- Po‐Tso Lin (ORCID: https://orcid.org/0000-0002-1985-8352)
- Yen‐Cheng Shih (ORCID: https://orcid.org/0000-0002-2993-2739)
- Hsiang‐Yu Yu (ORCID: https://orcid.org/0000-0002-8183-1863)
- Chin-Jou Li (ORCID: https://orcid.org/0009-0007-8221-4553)
- Jen-Cheng Hou (ORCID: https://orcid.org/0000-0002-4010-0220)
- Chien-Chen Chou (ORCID: https://orcid.org/0000-0002-0606-377X)
- Stéphane Dufau
Institutions
- Centre National de la Recherche Scientifique (FR)
- National Yang Ming Chiao Tung University (TW)
- National Taiwan University of Science and Technology (TW)
- The University of Queensland (AU)
- National Taiwan University (TW)
- Mater Misericordiae Hospital (AU)
- Research Center for Information Technology Innovation, Academia Sinica (TW)
- Mater Research (AU)
Publication Details
- Journal
- Epilepsy Research
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1016/j.eplepsyres.2024.107453
- Citations
- 1
- Primary Topic
- Face recognition and analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.25