Towards Continuous Health Diagnosis from Faces with Deep Learning

Martin V., Seguier R., Porcheron A., Morizot F.

1st International Workshop on PRedictive Intelligence in MEdicine (PRIME), Granada, Nicaragua, 16 September 2018, vol.11121, pp.120-128 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 11121
  • Doi Number: 10.1007/978-3-030-00320-3_15
  • City: Granada
  • Country: Nicaragua
  • Page Numbers: pp.120-128
  • Istanbul Technical University Affiliated: No


Recent studies show that health perception from faces by humans is a good predictor of good health and healthy behaviors. We aimed to automatize human health perception by training a Convolutional Neural Network on a related task (age estimation) combined with a Ridge Regression to rate faces. Indeed, contrary to health ratings, large datasets with labels of biological age exist. The results show that our system outperforms average human judgments for health. The system could be used on a daily basis to detect early signs of sickness or a declining state. We are convinced that such a system will contribute to more extensively explore the use of holistic, fast, and non-invasive measures to improve the speed of diagnosis.