NDLI: Orthogonal enhanced linear discriminant analysis for face recognition

Content Provider	IET Digital Library
Author	Lin, Chuang Wang, Binghui Fan, Xin Ma, Yanchun Liu, Huiyun
Abstract	From the intuition that natural face images lie on or near a low-dimensional submanifold, the authors propose a novel spectral graph based dimensionality reduction method, named orthogonal enhanced linear discriminant analysis (OELDA), for face recognition. OELDA is based on enhanced LDA (ELDA), which takes into account both the discriminative structure and geometrical structure of the face space, and generates non-orthogonal basis vectors. However, a significant fact is that eliminating the dependence of basis vectors can promote more effective recognition of unseen face images. For this purpose, the authors seek to improve the ELDA scheme by imposing orthogonal constraints on the basis vectors. Experimental results on real-world face datasets show that, benefitting from orthogonality, OELDA has more locality preserving power and discriminative power than LDA and ELDA, and achieves the highest recognition rates among compared methods.
Starting Page	100
Ending Page	110
Page Count	11
ISSN	20474938
Volume Number	5
e-ISSN	20474946
Issue Number	Issue 2, Jun (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/5/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2014.0086
Journal	IET Biometrics
Publisher Date	2016-06-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Combinatorial Mathematics Computer Vision And Image Processing Technique Dimensionality Reduction Method Discriminative Structure Face Recognition Face Space Geometrical Structure Graph Theory Image Recognition Natural Face Image Novel Spectral Graph OELDA Orthogonal Enhanced Linear Discriminant Analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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