  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.
