NDLI: Application of bidirectional two-dimensional principal component analysis to curvelet feature based face recognition

Content Provider	IEEE Xplore Digital Library
Author	Mohammed, A.A. Wu, Q.M.J. Sid-Ahmed, M.A.
Copyright Year	2009
Description	Author affiliation: Department of Electrical and Computer Engineering, Windsor, Ontario, Canada (Mohammed, A.A.; Wu, Q.M.J.; Sid-Ahmed, M.A.)
Abstract	A bidirectional two-dimensional principal component analysis (2DPCA) is proposed for human face recognition using curvelet feature subspace. Traditionally multiresolution analysis tools namely wavelets and curvelets have been used in the past for extracting and analyzing still images for recognition and classification tasks. Curvelet transform has gained significant popularity over wavelet based techniques due to its improved directional and edge representation capability. In the past features extracted from curvelet subbands were dimensionally reduced using linear principal component analysis (PCA) for obtaining a representative feature set. The novelty of the proposed method lies in the application of 2DPCA to curvelet feature subspace by computing image covariance matrices of square training sample matrices in their original form and transposed form respectively to generate a more meaningful and enhanced feature vectors. Extensive experiments were performed using the proposed bidirectional 2DPCA based face recognition algorithm and superior performance is obtained in comparison with state of the art techniques.
Starting Page	4124
Ending Page	4130
File Size	674707
Page Count	7
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5346723
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Face recognition Covariance matrix Humans Multiresolution analysis Wavelet analysis Image analysis Image recognition Wavelet transforms Feature extraction discrete curvelet transform multi-resolution tools AdaBoost
Content Type	Text
Resource Type	Article

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
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