NDLI: A novel random projection model for Linear Discriminant Analysis based face recognition

Content Provider	IEEE Xplore Digital Library
Author	Hui Liu Wen-Sheng Chen
Copyright Year	2009
Description	Author affiliation: Department of Information & Computational Science, Shenzhen University, 518060, China (Hui Liu; Wen-Sheng Chen)
Abstract	Linear Discriminant Analysis (LDA) is one of the commonly used statistical methods for feature extraction in face recognition tasks. However, LDA often suffers from the Small Sample Size (3S) problem, which occurs when the total number of training data is smaller than the dimension of input feature space. To deal with 3S problem, this paper proposes a novel approach for LDA-based face recognition using Random Projection (RP) technique. The advantages of random projection mainly include three aspects such as data-independent, dimensionality reduction and approximate distance preservation. So, based on the Johnson-Lindenstrauss theory, a new RP model is proposed for dimensionality reduction and simultaneously for learning the structure of the manifold with high accuracy. If the within-class scatter matrix is nonsingular in the randomly mapped feature space, LDA can be performed directly. Otherwise, RP will be followed by our previous Regularized Discriminant Analysis (RDA) approach for face recognition. Two public available databases, namely FERET and CMU PIE databases, are selected for evaluation. Comparing with PCA, DLDA and Fisherface approaches, our proposed method gives the best performance.
Starting Page	112
Ending Page	117
File Size	291470
Page Count	6
File Format	PDF
ISBN	9781424437283
DOI	10.1109/ICWAPR.2009.5207431
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Small sample size problem Face recognition Null space Training data Scattering Wavelet analysis Spatial databases Eigenvalues and eigenfunctions Random projection Linear discriminant analysis Pattern recognition Principal component analysis
Content Type	Text
Resource Type	Article

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