A Health Detection Model Based on Facial Data

Document Type

Conference Proceeding

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Publication Title

Advances in Intelligent Systems and Computing

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The purpose of this study is to develop a model which employs the facial expressions and features of people to predict their health. Our objective is to find the best Machine learning approaches to develop a health model which utilizes the facial features. This report also discusses the available datasets of facial expressions. Here, we utilize such machine learning techniques as regression, neural network, and clustering to predict symptoms of sickness. To construct the model, we train our model with the healthy people images acquired from JAFFE database. After that, we ran the test dataset that includes an equal amount of sick and healthy people images. Utilizing the CCN (convolutional neural network) approach, our model has been able to predict the health of a person based on the facial features with an accuracy of 70%. This model could be utilized as the first level of diagnosis and can be implemented to distinguish between a healthy and sick person at the entrance of the public facilities. Such information could be crucial in the prevention and control of infectious diseases.


Computer vision, Computer-aided, Disease detection, Facial features, Facial expressions, Image classification, Image segmentation, Neural network, Preliminary diagnosis, Temporal variations


Models and Methods | Pathological Conditions, Signs and Symptoms

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