Deep learning predicted perceived age is a reliable approach for analysis of facial ageing: A proof of principle study

Abstract

Background: Perceived age (PA) has been associated with mortality, genetic variants linked to ageing and several age-related morbidities. However, estimating PA in large datasets is laborious and costly to generate, limiting its practical applicability.

Objectives: To determine if estimating PA using deep learning-based algorithms results in the same associations with morbidities and genetic variants as human-estimated perceived age.

Methods: Self-supervised learning (SSL) and deep feature transfer (DFT) deep learning (DL) approaches were trained and tested on human-estimated PAs and their corresponding frontal face images of middle-aged to elderly Dutch participants (n = 2679) from a population-based study in the Netherlands. We compared the DL-estimated PAs with morbidities previously associated with human-estimated PA as well as genetic variants in the gene MC1R; we additionally tested the PA associations with MC1R in a new validation cohort (n = 1158).

Results: The DL approaches predicted PA in this population with a mean absolute error of 2.84 years (DFT) and 2.39 years (SSL). In the training-test dataset, we found the same significant (p < 0.05) associations for DL PA with osteoporosis, ARHL, cognition, COPD and cataracts and MC1R, as with human PA. We also found a similar but less significant association for SSL and DFT PAs (0.69 and 0.71 years per allele, p = 0.008 and 0.011, respectively) with MC1R variants in the validation dataset as that found with human, SSL and DFT PAs in the training-test dataset (0.79, 0.78 and 0.71 years per allele respectively; all p < 0.0001).

Conclusions: Deep learning methods can automatically estimate PA from facial images with enough accuracy to replicate known links between human-estimated perceived age and several age-related morbidities. Furthermore, DL predicted perceived age associated with MC1R gene variants in a validation cohort. Hence, such DL PA techniques may be used instead of human estimations in perceived age studies thereby reducing time and costs.

FIGURE 1

Dataset from the Rotterdam Study for the training and validation of DL algorithms. (a) Datasets used for training and testing the two DL approaches and an additional dataset for genetic validation associations with SNPs in the MC1R gene. Figure (b) presents represents the dataset used for the two DL methods with the Rrndom initiation, face recognition and chronological age being external datasets.

FIGURE 2

Average 2D facial photos of women looking older (a) and younger (b) than their chronological age. The figure presents the 2D average face of seven women that looked older than the chronological age (years) and seven women that looked younger than their chronological age. Below, the mean age and standard deviation of the ages are presented. With columns showing the mean chronological age (and standard deviation), as well as their mean perceive age estimated our previous study and as estimated using DL as described in this manuscript. SSL refers to self‐supervised learning and DFT refers to deep feature transfer. The average faces were derived using publicly available software (details presented in the Appendix S1).