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.
CITATION STYLE
Martin, V., Séguier, R., Porcheron, A., & Morizot, F. (2018). Towards Continuous Health Diagnosis from Faces with Deep Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11121 LNCS, pp. 120–128). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-00320-3_15
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