NDLI: Low-resolution face recognition and feature selection based on multidimensional scaling joint L 2,1-norm regularisation

Content Provider	IET Digital Library
Author	Li, Fei Jiang, Mingyan
Abstract	Face recognition is confronted with situations wherein the captured face images are not as clear as the registered ones; this is known as low-resolution face recognition. To solve this problem, the authors propose a new sparse coupled projection method using multidimensional scaling joint L 2,1-norm regularisation (MDSL21). The MDSL21 maps the low-resolution faces and high-resolution faces into a common sparse subspace in which feature selection and coupled transformation can be simultaneously achieved. In their proposed method, the authors first learn the common responds using the multidimensional scaling model. Specifically, the distance between the responds is approximated to the distance between the high-resolution samples, and the local manifolds are preserved. Then, the seeking coupled projections are formulated as a regression model. Inspired by the sparse constraint utilised in the classical subspace learning methods, the authors add an L 2,1-norm regularisation term to the regression model to realise the sparsity and present its optimisation method. Experimental results validate the effectiveness of their proposed method on the low-resolution face recognition task.
Starting Page	198
Ending Page	205
Page Count	8
ISSN	20474938
Volume Number	8
e-ISSN	20474946
Issue Number	Issue 3, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/8/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2018.5044
Journal	IET Biometrics
Publisher Date	2019-01-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Captured Face Image Classical Subspace Learning Method Computer Vision And Image Processing Technique Face Recognition Feature Extraction Feature Selection High-resolution Sample Image Recognition Image Resolution Knowledge Engineering Technique Learning in AI Low-resolution Face Recognition Task MDSL21 Maps Multidimensional Scaling Joint L2,1-norm Regularisation Multidimensional Scaling Model Norm Regularisation Term Optimisation Optimisation Method Regression Analysis Regression Model Sparse Coupled Projection Method Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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