Nonparametric Discriminant Analysis for Face Recognition

In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussia...

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Vydané v:IEEE transactions on pattern analysis and machine intelligence Ročník 31; číslo 4; s. 755 - 761
Hlavní autori: Li, Zhifeng, Lin, Dahua, Tang, Xiaoou
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Los Alamitos, CA IEEE 01.04.2009
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Applied sciences
Artificial intelligence
Boolean functions
Classifier design and evaluation
Computer science; control theory; systems
Data structures
Degradation
Discriminant Analysis
Exact sciences and technology
Face
Face and gesture recognition
Face recognition
Gaussian distribution
Humans
Image databases
Linear discriminant analysis
Mathematical analysis
Matrices
Matrix methods
Null space
Pattern Recognition, Automated - statistics & numerical data
Pattern recognition. Digital image processing. Computational geometry
Scatter
Scattering
Spatial databases
Statistics, Nonparametric
Studies
Gabor filter
Discriminant analysis
Image recognition
Face recognition
Image processing
classifier design and evaluation
Image databank
Non gaussian noise
Gaussian distribution
Subspace method
Autoregressive model
Regression analysis
Pattern recognition
multiclassifier fusion
nonparametric
Classification
Facies
Database
Pattern analysis
Artificial intelligence
Multiclass
discriminant analysis (NDA)
ISSN:0162-8828, 1939-3539
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Popis
Shrnutí:In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussian distribution assumption. The performance of these methods notably degrades when the actual distribution is Non-Gaussian. To address this problem, we propose a new formulation of scatter matrices to extend the two-class nonparametric discriminant analysis to multi-class cases. Then, we develop two more improved multi-class NDA-based algorithms (NSA and NFA) with each one having two complementary methods based on the principal space and the null space of the intra-class scatter matrix respectively. Comparing to the NSA, the NFA is more effective in the utilization of the classification boundary information. In order to exploit the complementary nature of the two kinds of NFA (PNFA and NNFA), we finally develop a dual NFA-based multi-classifier fusion framework by employing the over complete Gabor representation to boost the recognition performance. We show the improvements of the developed new algorithms over the traditional subspace methods through comparative experiments on two challenging face databases, Purdue AR database and XM2VTS database.
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ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2008.174