Author(s)

Meng Sang ^1,2, Yang Yang ^1,2

Affiliation(s)

¹ School of Information Science and Technology, Yunnan Normal University, Kunming, China
² Laboratory of Pattern Recognition and Artificial Intelligence, Yunnan Normal University, Kunming, China

Corresponding Author

Yang Yang

ABSTRACT

Mining of hard samples has always been a challenge in the field of face recognition. Mining-based methods have achieved promising results on the challenge of hard samples. However, current methods all suffer from the problem of not thinking, about when the hard samples should be close to the target class center and when they should be close to the non-target class center. Therefore, this work is based on CurricularFace by analyzing the logit and gradient, to carry out the boundary of judging the hard samples to be close to the center of the target class and non-target class to be close to the center, and based on the boundary to revisit the CurricularFace, to obtain a revised CurricularFace (ReCurricularFace), which is named as ReCurricularFace. We find through comparison experiments that ReCurricularFace obtains a huge improvement in the face benchmark.

KEYWORDS

Face recognition, deep learning, loss function

CITE THIS PAPER

Meng Sang, Yang Yang, ReCurricularFace: Revisiting CurricularFace for Hard Sample Mining. Advances in Computer, Signals and Systems (2024) Vol. 8: 1-6. DOI: http://dx.doi.org/10.23977/acss.2024.080201.

REFERENCES

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