Estimating Remaining Lifespan from the Face
This work addresses lifespan estimation for individuals using facial data, with potential applications in health and policy, but it is incremental as it builds on existing CNN methods.
The study tackled predicting a person's remaining lifespan from facial images by fine-tuning CNN models on a dataset of over 24,000 images, achieving a mean absolute error of 8.3 years in validation, though performance decreased for younger individuals.
The face is a rich source of information that can be utilized to infer a person's biological age, sex, phenotype, genetic defects, and health status. All of these factors are relevant for predicting an individual's remaining lifespan. In this study, we collected a dataset of over 24,000 images (from Wikidata/Wikipedia) of individuals who died of natural causes, along with the number of years between when the image was taken and when the person passed away. We made this dataset publicly available. We fine-tuned multiple Convolutional Neural Network (CNN) models on this data, at best achieving a mean absolute error of 8.3 years in the validation data using VGGFace. However, the model's performance diminishes when the person was younger at the time of the image. To demonstrate the potential applications of our remaining lifespan model, we present examples of using it to estimate the average loss of life (in years) due to the COVID-19 pandemic and to predict the increase in life expectancy that might result from a health intervention such as weight loss. Additionally, we discuss the ethical considerations associated with such models.