NDLI: LRR-TTK DL for face recognition

Content Provider	IET Digital Library
Author	Pan, Yuqi Jiang, Mingyan
Abstract	Dictionary learning (DL) technique has received a great interest recently, due to its significant role in feature extraction. Although many DL-based methods have been presented, some of them still suffer from the lack of discriminative features, especially for the local manifold features. To mitigate this problem, the authors propose a novel DL method named low-rank representation based on twin tensor kernel (LRR-TTK) DL for face recognition in this study. Specifically, the training samples are projected to a high-dimensional space with TTK. Then, they extract the local manifold features and spatial features (representation coefficients) hidden in the facial images by TT locality preserving projection. In addition, powered by LRR reconstruction and DL theory, much more discriminative features are obtained, which can improve the recognition rate greatly. Comprehensive experimental results at AR, extended Yale-B and FERET face databases demonstrate the superiority of their proposed method.
Starting Page	165
Ending Page	172
Page Count	8
ISSN	20474938
Volume Number	6
e-ISSN	20474946
Issue Number	Issue 3, May (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/6/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2016.0081
Journal	IET Biometrics
Publisher Date	2016-11-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra AR Face Database Computer Vision And Image Processing Technique Dictionary Learning Technique DL Theory Extended Yale-B Face Database Face Recognition Feature Extraction FERET Face Database Image Recognition Image Representation Knowledge Engineering Technique Learning in AI Local Manifold Feature Extraction Low-rank Representation LRR Reconstruction LRR-TTK DL Representation Coefficient Spatial Feature Extraction Tensors TT Locality Preserving Projection Twin Tensor Kernel
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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