Due to the rising concern of data privacy, it's reasonable to assume the
local client data can't be transferred to a centralized server, nor their
associated identity label is provided. To support continuous learning and fill
the last-mile quality gap, we introduce a new problem setup called
Local-Adaptive Face Recognition (LaFR). Leveraging the environment-specific
local data after the deployment of the initial global model, LaFR aims at
getting optimal performance by training local-adapted models automatically and
un-supervisely, as opposed to fixing their initial global model. We achieve
this by a newly proposed embedding cluster model based on Graph Convolution
Network (GCN), which is trained via meta-optimization procedure. Compared with
previous works, our meta-clustering model can generalize well in unseen local
environments. With the pseudo identity labels from the clustering results, we
further introduce novel regularization techniques to improve the model
adaptation performance. Extensive experiments on racial and internal sensor
adaptation demonstrate that our proposed solution is more effective for
adapting face recognition models in each specific environment. Meanwhile, we
show that LaFR can further improve the global model by a simple federated
aggregation over the updated local models.

本文提出了一种基于环境数据的本地自适应人脸识别方法（LaFR），采用基于图卷积网络（GCN）的嵌入式聚类模型，在非监督学习方式下自动训练本地适应模型，并通过新型正则化技术提高模型适应性能，从而实现在不泄露本地客户隐私的前提下，提高人脸识别模型在特定环境下的准确性能。