Total Cluster
Makarand Tapaswi, Omkar Parkhi, Esa Rahtu, Eric Sommerlade, Rainer Stiefelhagen, Andrew Zisserman · 2014
The goal of this paper is unsupervised face clustering in edited video material – where face tracks arising from different people are assigned to separate clusters, with one cluster for each person. In particular we explore the extent to which faces can be clustered automatically without making an error. This is a very challenging problem given the variation in pose, lighting and expressions that can occur, and the similarities between different people.