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Towards Continuous Health Diagnosis from Faces with Deep Learning

  • Victor MartinEmail author
  • Renaud Séguier
  • Aurélie Porcheron
  • Frédérique Morizot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11121)

Abstract

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.

Keywords

Health estimation Non-invasive diagnosis Convolutional Neural Network Facial features 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.CentraleSupelecCesson-SévignéFrance
  2. 2.CHANEL Parfums BeautéPantinFrance

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