NDLI: Sparsifying transform learning for face image classification

Content Provider	IET Digital Library
Author	Qudaimat, A. Demirel, H.
Abstract	Sparse signal representation showed promising results in the field of face recognition in the past few years. An algorithm based on a sparsifying transform is considered. It mainly learns a dictionary that can transform the image into sparse vectors. In the transformation domain, the images of the same class should have similar non-zero coefficients pattern that can be used for identification. The classification process of this method only requires to transform the image and make norm comparisons to determine the class of the image. The proposed method shows a comparable performance with the other known methods in the literature by means of accuracy. A novel method in sparsity-based image identification that uses analysis dictionaries is proposed, unlike the conventional sparsity-based methods. One advantage of the proposed algorithm is the low computational cost of the classification process.
Starting Page	1034
Ending Page	1036
Page Count	3
ISSN	00135194
Volume Number	54
e-ISSN	1350911X
Issue Number	Issue 17, Aug (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/54/17
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2018.0524
Journal	Electronics Letters
Publisher Date	2018-07-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Analysis Dictionaries Classification Process Comparable Performance Computer Vision And Image Processing Technique Conventional Sparsity-based Method Dictionary Face Image Classification Face Recognition Image Classification Image Representation Integral Transforms Known Method Learning in AI Nonzero Coefficient Pattern Norm Comparisons Optical, Image And Video Signal Processing Optimisation Technique Signal Reconstruction Signal Representation Sparse Signal Representation Sparse Vector Sparsifying Sparsity-based Image Identification Statistics Transform Transformation Domain
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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