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Despite the unprecedented improvement of face recognition, existing facerecognition models still show considerably low performances in determiningwhether a pair of child and adult images belong to the same identity. Previousapproaches mainly focused on increasing the similarity between child and adultimages of a given identity to overcome the discrepancy of facial appearancesdue to aging. However, we observe that reducing the similarity between childimages of different identities is crucial for learning distinct features amongchildren and thus improving face recognition performance in child-adult pairs.Based on this intuition, we propose a novel loss function called theInter-Prototype loss which minimizes the similarity between child images.Unlike the previous studies, the Inter-Prototype loss does not requireadditional child images or training additional learnable parameters. Ourextensive experiments and in-depth analyses show that our approach outperformsexisting baselines in face recognition with child-adult pairs. Our code andnewly-constructed test sets of child-adult pairs are available athttps://github.com/leebebeto/Inter-Prototype.

Improving Face Recognition with Large Age Gaps by Learning to Distinguish Children