Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning

We develop a parameter-free face recognition algorithm which is insensitive to large variations in lighting, expression, occlusion, and age using a single gallery sample per subject. We take advantage of the observation that equidistant prototypes embedding is an optimal embedding that maximizes the...

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Vydané v:	Pattern recognition Ročník 47; číslo 12; s. 3738 - 3749
Hlavní autori:	Deng, Weihong, Hu, Jiani, Zhou, Xiuzhuang, Guo, Jun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ltd 01.12.2014 Elsevier
Predmet:	Applied sciences Detection, estimation, filtering, equalization, prediction Exact sciences and technology Face recognition Feature extraction Generic learning Image processing Information, signal and communications theory Linear regression One sample problem Pattern recognition Signal and communications theory Signal processing Signal, noise Telecommunications and information theory One sample problem Feature extraction Generic learning Face recognition Linear regression Biometrics Performance evaluation State of the art Prototype Image processing Updating Mapping Regression analysis Pattern recognition Algorithm Learning Least squares method Database Signal processing Localization Automatic recognition
ISSN:	0031-3203, 1873-5142
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