MixFace: Improving Face Verification Focusing on Fine-grained Conditions

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 datase...

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Bibliographic Details
Main Authors Jung, Junuk, Son, Sungbin, Park, Joochan, Park, Yongjun, Lee, Seonhoon, Oh, Heung-Seon
Format Journal Article
LanguageEnglish
Published 02.11.2021
Subjects
Computer Science - Computer Vision and Pattern Recognition
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Summary: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.
DOI:10.48550/arxiv.2111.01717