The performance of face recognition has become saturated for public benchmark datasets such as LFW, CFP-FP, and AgeDB, owing to the rapid advances in CNNs. However, the effects of faces with various fine-grained conditions on FR models have not been investigated because of the absence of such datasets. This paper analyzes their effects in terms of different conditions and loss functions using K-FACE, a recently introduced FR dataset with fine-grained conditions. We propose a novel loss function, MixFace, that combines classification and metric losses. The superiority of MixFace in terms of effectiveness and robustness is demonstrated experimentally on various benchmark datasets.
@article{arxiv.2111.01717,
title = {MixFace: Improving Face Verification Focusing on Fine-grained Conditions},
author = {Junuk Jung and Sungbin Son and Joochan Park and Yongjun Park and Seonhoon Lee and Heung-Seon Oh},
journal= {arXiv preprint arXiv:2111.01717},
year = {2022}
}