Discriminative Multimanifold Analysis for Face Recognition from a Single Training Sample per Person

Conventional appearance-based face recognition methods usually assume that there are multiple samples per person (MSPP) available for discriminative feature extraction during the training phase. In many practical face recognition applications such as law enhancement, e-passport, and ID card identifi...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 35; číslo 1; s. 39 - 51
Hlavní autori:	Lu, Jiwen, Tan, Yap-Peng, Wang, Gang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Los Alamitos, CA IEEE 01.01.2013 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Applied sciences Artificial Intelligence Biometrics Biometry - methods Computer science; control theory; systems Data processing. List processing. Character string processing Discriminant Analysis Educational institutions Effectiveness Exact sciences and technology Face Face - anatomy & histology Face recognition Feature extraction Humans Image Interpretation, Computer-Assisted - methods Intelligence Learning Learning and adaptive systems manifold learning Manifolds Memory organisation. Data processing Pattern analysis Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Sample Size Semantics Signal Processing, Computer-Assisted single training sample per person Software subspace learning Subtraction Technique Training single training sample per person Computer vision Discriminant analysis Image processing Face recognition Image databank Subspace method manifold learning subspace learning Dimension reduction Image analysis Experimental result Multidimensional analysis Official document Facies Selection criterion Feature extraction Reduced order model Pattern extraction Electronic government
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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