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.
翻译:脸部是一个丰富的信息来源,可以用来推断一个人的生物年龄、性别、苯型、遗传缺陷和健康状况。所有这些因素都与预测一个人的剩余寿命有关。在本研究中,我们收集了24 000多张自然死亡个人图像的数据集(来自维基数据/维基pedia),以及从图像拍摄到人去世之间的年数。我们公布了这一数据集。我们对这一数据的多重革命神经网络模型进行了细微调整,在使用VGGFace的验证数据中,最理想地实现了8.3年的绝对误差。然而,模型的性能在人年轻时会减少。为了展示我们剩余寿命模型的潜在应用,我们举例地用它来估计COVID-19大流行造成的平均生命损失(年数),并预测因体重损失等健康干预而可能增加的预期寿命。我们讨论了与这些模型有关的伦理因素。