Open-set Face Recognition using Ensembles trained on Clustered Data
Rafael Henrique Vareto William Robson Schwartz
Open-set face recognition describes a scenario where unknown subjects, unseen
during the training stage, appear on test time. Not only it requires methods
that accurately identify individuals of interest, but also demands approaches
that effectively deal with unfamiliar faces. This work details a scalable
open-set face identification approach to galleries composed of hundreds and
thousands of subjects. It is composed of clustering and an ensemble of binary
learning algorithms that estimates when query face samples belong to the face
gallery and then retrieves their correct identity. The approach selects the
most suitable gallery subjects and uses the ensemble to improve prediction
performance. We carry out experiments on well-known LFW and YTF benchmarks.
Results show that competitive performance can be achieved even when targeting
scalability.