Laplacian-Uniform Mixture-Driven Iterative Robust Coding With Applications to Face Recognition Against Dense Errors

Outliers due to occlusion, pixel corruption, and so on pose serious challenges to face recognition despite the recent progress brought by sparse representation. In this article, we show that robust statistics implemented by the state-of-the-art methods are insufficient for robustness against dense g...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 31; číslo 9; s. 3620 - 3633
Hlavní autori:	Zheng, Huicheng, Lin, Dajun, Lian, Lina, Dong, Jiayu, Zhang, Peipei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.09.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation algorithms Coding Coding residual Corruption Encoding Error correction Error correction & detection Error detection Face Face recognition Facial recognition technology Iterative methods Minimization non-convexity Objective function Occlusion outlier Outliers (statistics) Pattern recognition Representations Robustness sparse representation Training
ISSN:	2162-237X, 2162-2388, 2162-2388
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