This paper presents a novel approach for accurately estimating age from face images, which overcomes the challenge of collecting a large dataset of individuals with the same identity at different ages. Instead, we leverage readily available face datasets of different people at different ages and aim to extract age-related features using contrastive learning. Our method emphasizes these relevant features while suppressing identity-related features using a combination of cosine similarity and triplet margin losses. We demonstrate the effectiveness of our proposed approach by achieving state-of-the-art performance on two public datasets, FG-NET and MORPH-II.
@article{arxiv.2308.11896,
title = {Age Prediction From Face Images Via Contrastive Learning},
author = {Yeongnam Chae and Poulami Raha and Mijung Kim and Bjorn Stenger},
journal= {arXiv preprint arXiv:2308.11896},
year = {2023}
}