Kernel difference maximisation-based sparse representation for more accurate face recognition

Kernel difference maximisation-based sparse representation for more accurate face recognition

Most methods for sparse representation are designed to be used in the original space. However, their performance is not always satisfactory especially when training samples are limited. According to the previous studies, more information can be obtained from samples in the feature space than those i...

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Bibliographic Details
Main Authors: Lian Wu, Wenbo Xu, Jianchuan Zhao, Zhongwei Cui, Yong Zhao
Format: Article
Language:English
Published: Wiley 2020-11-01
Series:The Journal of Engineering
Subjects:
feature extraction
learning (artificial intelligence)
face recognition
image representation
image classification
optimisation
l(2) regularisation-based sparse solution
training sample linear combinations
test sample classification
novel kernel difference maximisation-based sparse representation method
feature space
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1003

Internet

https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1003

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