Federated Unsupervised Domain Adaptation for Face Recognition
- URL: http://arxiv.org/abs/2204.04382v1
- Date: Sat, 9 Apr 2022 04:02:03 GMT
- Title: Federated Unsupervised Domain Adaptation for Face Recognition
- Authors: Weiming Zhuang, Xin Gan, Yonggang Wen, Xuesen Zhang, Shuai Zhang,
Shuai Yi
- Abstract summary: We propose federated unsupervised domain adaptation for face recognition, FedFR.
For unlabeled data in the target domain, we enhance a clustering algorithm with distance constrain to improve the quality of predicted pseudo labels.
We also propose a new domain constraint loss to regularize source domain training in federated learning.
- Score: 26.336693850812118
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Given labeled data in a source domain, unsupervised domain adaptation has
been widely adopted to generalize models for unlabeled data in a target domain,
whose data distributions are different. However, existing works are
inapplicable to face recognition under privacy constraints because they require
sharing of sensitive face images between domains. To address this problem, we
propose federated unsupervised domain adaptation for face recognition, FedFR.
FedFR jointly optimizes clustering-based domain adaptation and federated
learning to elevate performance on the target domain. Specifically, for
unlabeled data in the target domain, we enhance a clustering algorithm with
distance constrain to improve the quality of predicted pseudo labels. Besides,
we propose a new domain constraint loss (DCL) to regularize source domain
training in federated learning. Extensive experiments on a newly constructed
benchmark demonstrate that FedFR outperforms the baseline and classic methods
on the target domain by 3% to 14% on different evaluation metrics.
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