Open-set Face Recognition using Ensembles trained on Clustered Data

TL;DR

This paper presents a scalable open-set face recognition method using clustering and ensemble learning to accurately identify known faces and handle unknown subjects in large galleries.

Contribution

It introduces a novel scalable approach combining clustering and ensemble of binary classifiers for open-set face recognition in large galleries.

Findings

Achieves competitive accuracy on LFW and YTF benchmarks.

Effectively handles unknown faces in large-scale galleries.

Improves prediction performance with ensemble methods.

Abstract

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.