Bell’s Palsy Severity Determination by Computer Vision and Machine Learning
This project heavily relies on Emotrics, which is a machine-learning-based facial landmark predictor that allows us to extract facial landmarks from images.
References
- Guarin, D. L., Dusseldorp, J., Hadlock, T. A., & Jowett, N. (2018). A machine learning approach for automated facial measurements in facial palsy. JAMA facial plastic surgery, 20(4), 335-337.
- Guarin, Diego L., et al. “Toward an Automatic System for Computer-Aided Assessment in Facial Palsy.” Facial plastic surgery & aesthetic medicine vol. 22, 1 (2020): 42-49. doi:10.1089/fpsam.2019.29000.gua
- Parra-Dominguez, et al. “Facial Paralysis Detection on Images Using Key Point Analysis.” Appl. Sci. 2021, 11, 2435. https://doi.org/10.3390/app11052435
- Greene, Jacqueline J., et al. “The spectrum of facial palsy: The MEEI facial palsy photo and video standard set.” The Laryngoscope vol. 130, 1 (2020): 32-37. doi:10.1002/lary.27986
- Bandini, Andrea, et al. “A New Dataset for Facial Motion Analysis in Individuals With Neurological Disorders.” IEEE journal of biomedical and health informatics vol. 25, 4 (2021): 1111-1119. doi:10.1109/JBHI.2020.3019242