18 research outputs found
Learning Discriminative Features with Class Encoder
Deep neural networks usually benefit from unsupervised pre-training, e.g.
auto-encoders. However, the classifier further needs supervised fine-tuning
methods for good discrimination. Besides, due to the limits of full-connection,
the application of auto-encoders is usually limited to small, well aligned
images. In this paper, we incorporate the supervised information to propose a
novel formulation, namely class-encoder, whose training objective is to
reconstruct a sample from another one of which the labels are identical.
Class-encoder aims to minimize the intra-class variations in the feature space,
and to learn a good discriminative manifolds on a class scale. We impose the
class-encoder as a constraint into the softmax for better supervised training,
and extend the reconstruction on feature-level to tackle the parameter size
issue and translation issue. The experiments show that the class-encoder helps
to improve the performance on benchmarks of classification and face
recognition. This could also be a promising direction for fast training of face
recognition models.Comment: Accepted by CVPR2016 Workshop of Robust Features for Computer Visio
Effective Face Frontalization in Unconstrained Images
"Frontalization" is the process of synthesizing frontal facing views of faces
appearing in single unconstrained photos. Recent reports have suggested that
this process may substantially boost the performance of face recognition
systems. This, by transforming the challenging problem of recognizing faces
viewed from unconstrained viewpoints to the easier problem of recognizing faces
in constrained, forward facing poses. Previous frontalization methods did this
by attempting to approximate 3D facial shapes for each query image. We observe
that 3D face shape estimation from unconstrained photos may be a harder problem
than frontalization and can potentially introduce facial misalignments.
Instead, we explore the simpler approach of using a single, unmodified, 3D
surface as an approximation to the shape of all input faces. We show that this
leads to a straightforward, efficient and easy to implement method for
frontalization. More importantly, it produces aesthetic new frontal views and
is surprisingly effective when used for face recognition and gender estimation