15 research outputs found
BioMetricNet: deep unconstrained face verification through learning of metrics regularized onto Gaussian distributions
We present BioMetricNet: a novel framework for deep unconstrained face
verification which learns a regularized metric to compare facial features.
Differently from popular methods such as FaceNet, the proposed approach does
not impose any specific metric on facial features; instead, it shapes the
decision space by learning a latent representation in which matching and
non-matching pairs are mapped onto clearly separated and well-behaved target
distributions. In particular, the network jointly learns the best feature
representation, and the best metric that follows the target distributions, to
be used to discriminate face images. In this paper we present this general
framework, first of its kind for facial verification, and tailor it to Gaussian
distributions. This choice enables the use of a simple linear decision boundary
that can be tuned to achieve the desired trade-off between false alarm and
genuine acceptance rate, and leads to a loss function that can be written in
closed form. Extensive analysis and experimentation on publicly available
datasets such as Labeled Faces in the wild (LFW), Youtube faces (YTF),
Celebrities in Frontal-Profile in the Wild (CFP), and challenging datasets like
cross-age LFW (CALFW), cross-pose LFW (CPLFW), In-the-wild Age Dataset (AgeDB)
show a significant performance improvement and confirms the effectiveness and
superiority of BioMetricNet over existing state-of-the-art methods.Comment: Accepted at ECCV2