Joint Registration and Representation Learning for Unconstrained Face
Identification
Abstract
Recent advances in deep learning have resulted in
human-level performances on popular unconstrained face
datasets including Labeled Faces in the Wild and YouTube
Faces. To further advance research, IJB-A benchmark was
recently introduced with more challenges especially in the
form of extreme head poses. Registration of such faces is
quite demanding and often requires laborious procedures
like facial landmark localization. In this paper, we propose a Convolutional Neural Networks based data-driven
approach which learns to simultaneously register and represent faces. We validate the proposed scheme on template based unconstrained face identification. Here, a template contains multiple media in the form of images and
video frames. Unlike existing methods which synthesize
all template media information at feature level, we propose
to keep the template media intact. Instead, we represent
gallery templates by their trained one-vs-rest discriminative models and then employ a Bayesian strategy which optimally fuses decisions of all medias in a query template. We
demonstrate the efficacy of the proposed scheme on IJB-A,
YouTube Celebrities and COX datasets where our approach
achieves significant relative performance boosts of 3.6%,
21.6% and 12.8% respectively