NDLI: Face recognition using supervised probabilistic principal component analysis mixture model in dimensionality reduction without loss framework

Content Provider	IET Digital Library
Author	Ahmadkhani, Somaye Adibi, Peyman
Abstract	In this study, first a supervised version for probabilistic principal component analysis mixture model is proposed. Using this model, local linear underlying manifolds of data samples are obtained. These underlying manifolds are used in a dimensionality reduction without loss framework, for face recognition application. In this framework, the benefits of dimensionality reduction are used in the predictive model, while using the projection penalty idea, the loss of useful information will be minimised. The authors use support vector machine (SVM) and k-nearest neighbour (KNN) classifiers as the predictive models in this framework. To train and evaluate the proposed method, the well-known face databases are used. The experimental results show that the proposed method with SVM as the predictive model have the most average classification accuracy compared with many traditional methods which use predictive model SVM after dimensionality reduction, and also compared with the projection penalty idea used for linear and non-linear kernel-based dimensionality reduction methods. Moreover, their experiments show that the proposed method with KNN as predictive model is superior to the case that dimensionality reduction is performed, and then the KNN classifier is applied.
Starting Page	193
Ending Page	201
Page Count	9
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 3, Apr (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2014.0434
Journal	IET Computer Vision
Publisher Date	2016-04-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Face Recognition Image Classification Image Recognition K-nearest Neighbour Classifier KNN Classifier Knowledge Engineering Technique Mixture Model Nonlinear Kernel-based Dimensionality Reduction Method Predictive Model Principal Component Analysis Statistics Supervised Probabilistic Principal Component Analysis Mixture Model Support Vector Machine SVM
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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