Age Prediction From Face Images Via Contrastive Learning
Yeongnam Chae Poulami Raha Mijung Kim Bjorn Stenger
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.