191 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
A face and palmprint recognition approach based on discriminant DCT feature extraction
2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
Globally maximizing, locally minimizing : unsupervised discriminant projection with applications to face and palm biometrics
2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
An improved LDA approach
2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
The fundamentals of unimodal palmprint authentication based on a biometric system: A review
Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases
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
Multispectral palmprint recognition based on three descriptors: LBP, Shift LBP, and Multi Shift LBP with LDA classifier
Local Binary Patterns (LBP) are extensively used to analyze local texture
features of an image. Several new extensions to LBP-based texture descriptors
have been proposed, focusing on improving noise robustness by using different
coding or thresholding schemes. In this paper we propose three algorithms
(LBP), Shift Local Binary Pattern (SLBP), and Multi Shift Local Binary Pattern
(MSLBP),to extract features for palmprint images that help to obtain the best
unique and characteristic values of an image for identification. The Principal
Component Analysis (PCA) algorithm has been applied to reduce the size of the
extracted feature matrix in random space and in the matching process; the
Linear Discriminant Analysis (LDA) algorithm is used. Several experiments were
conducted on the large multispectral database (blue, green, red, and infrared)
of the University of Hong Kong. As result, distinguished and high results were
obtained where it was proved that, the blue spectrum is superior to all spectra
perfectly
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