Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61009
Title: Quadratic projection based feature extraction with its application to biometric recognition
Authors: Yan, Y
Wang, H
Chen, S
Cao, X
Zhang, D 
Keywords: Biometric recognition
Feature extraction
Lagrange duality
Quadratic projection
Semidefinite programming
Issue Date: 2016
Publisher: Elsevier
Source: Pattern recognition, 2016, v. 56, p. 40-49 How to cite?
Journal: Pattern recognition 
Abstract: 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 conventional 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 recognition 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 algorithms.
URI: http://hdl.handle.net/10397/61009
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2016.02.010
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