854 research outputs found
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This paper presents a novel quadratic projection based feature extraction
framework, where a set of quadratic matrices is learned to distinguish each
class from all other classes. We formulate quadratic matrix learning (QML) as a
standard semidefinite programming (SDP) problem. However, the con- ventional
interior-point SDP solvers do not scale well to the problem of QML for
high-dimensional data. To solve the scalability of QML, we develop an efficient
algorithm, termed DualQML, based on the Lagrange duality theory, to extract
nonlinear features. To evaluate the feasibility and effectiveness of the
proposed framework, we conduct extensive experiments on biometric recognition.
Experimental results on three representative biometric recogni- tion tasks,
including face, palmprint, and ear recognition, demonstrate the superiority of
the DualQML-based feature extraction algorithm compared to the current
state-of-the-art algorithm
Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) has been continuously evolving in
several areas like pattern recognition and information retrieval methods. It
factorizes a matrix into a product of 2 low-rank non-negative matrices that
will define parts-based, and linear representation of nonnegative data.
Recently, Graph regularized NMF (GrNMF) is proposed to find a compact
representation,which uncovers the hidden semantics and simultaneously respects
the intrinsic geometric structure. In GNMF, an affinity graph is constructed
from the original data space to encode the geometrical information. In this
paper, we propose a novel idea which engages a Multiple Kernel Learning
approach into refining the graph structure that reflects the factorization of
the matrix and the new data space. The GrNMF is improved by utilizing the graph
refined by the kernel learning, and then a novel kernel learning method is
introduced under the GrNMF framework. Our approach shows encouraging results of
the proposed algorithm in comparison to the state-of-the-art clustering
algorithms like NMF, GrNMF, SVD etc.Comment: This paper has been withdrawn by the author due to the terrible
writin
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
- …