NDLI: Subspace Approximation of Face Recognition Algorithms: An Empirical Study

Content Provider	IEEE Xplore Digital Library
Author	Mohanty, P. Sarkar, S. Kasturi, R. Phillips, P.J.
Copyright Year	2005
Abstract	We present a theory for constructing linear subspace approximations to face-recognition algorithms and empirically demonstrate that a surprisingly diverse set of face-recognition approaches can be approximated well by using a linear model. A linear model, built using a training set of face images, is specified in terms of a linear subspace spanned by, possibly nonorthogonal vectors. We divide the linear transformation used to project face images into this linear subspace into two parts: 1) a rigid transformation obtained through principal component analysis, followed by a nonrigid, affine transformation. The construction of the affine subspace involves embedding of a training set of face images constrained by the distances between them, as computed by the face-recognition algorithm being approximated. We accomplish this embedding by iterative majorization, initialized by classical MDS. Any new face image is projected into this embedded space using an affine transformation. We empirically demonstrate the adequacy of the linear model using six different face-recognition algorithms, spanning template-based and feature-based approaches, with a complete separation of the training and test sets. A subset of the face-recognition grand challenge training set is used to model the algorithms and the performance of the proposed modeling scheme is evaluated on the facial recognition technology (FERET) data set. The experimental results show that the average error in modeling for six algorithms is 6.3% at 0.001 false acceptance rate for the FERET fafb probe set which has 1195 subjects, the most among all of the FERET experiments. The built subspace approximation not only matches the recognition rate for the original approach, but the local manifold structure, as measured by the similarity of identity of nearest neighbors, is also modeled well. We found, on average, 87% similarity of the local neighborhood. We also demonstrate the usefulness of the linear model for algorithm-dependent indexing of face databases and find that it results in more than 20 times reduction in face comparisons for Bayesian, elastic bunch graph matching, and one proprietary algorithm.
Sponsorship	IEEE Signal Processing Society
Starting Page	734
Ending Page	748
Page Count	15
File Size	2176104
File Format	PDF
ISSN	15566013
Volume Number	3
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Approximation algorithms Iterative algorithms Linear approximation Vectors Principal component analysis Subspace constraints Embedded computing Testing Probes template reconstruction Affine approximation error in indexing face recognition indexing indexing face templates linear modeling local manifold structure multidimensional scaling security and privacy subspace approximation
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Safety, Risk, Reliability and Quality

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
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
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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