Improving Face Recognition with Large Age Gaps by Learning to Distinguish Children
This addresses a specific challenge in face recognition for applications like security or missing persons, but is incremental as it builds on prior work by focusing on a new aspect of the problem.
The paper tackles the problem of low performance in face recognition models when matching child and adult images of the same identity, and proposes a novel loss function that reduces similarity between child images of different identities, resulting in improved performance over existing baselines.
Despite the unprecedented improvement of face recognition, existing face recognition models still show considerably low performances in determining whether a pair of child and adult images belong to the same identity. Previous approaches mainly focused on increasing the similarity between child and adult images of a given identity to overcome the discrepancy of facial appearances due to aging. However, we observe that reducing the similarity between child images of different identities is crucial for learning distinct features among children and thus improving face recognition performance in child-adult pairs. Based on this intuition, we propose a novel loss function called the Inter-Prototype loss which minimizes the similarity between child images. Unlike the previous studies, the Inter-Prototype loss does not require additional child images or training additional learnable parameters. Our extensive experiments and in-depth analyses show that our approach outperforms existing baselines in face recognition with child-adult pairs. Our code and newly-constructed test sets of child-adult pairs are available at https://github.com/leebebeto/Inter-Prototype.