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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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 31; H. 4; S. 755 - 761
Hauptverfasser:	Li, Zhifeng, Lin, Dahua, Tang, Xiaoou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.04.2009 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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