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Reseach Article

Biometric Recognition using Facial Expressions

Published on August 2011 by Krishnendu S. Nair, Ankita Singh
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National Technical Symposium on Advancements in Computing Technologies
Foundation of Computer Science USA
NTSACT - Number 2
August 2011
Authors: Krishnendu S. Nair, Ankita Singh
8d34d6bf-d9fc-47e3-931f-bb7525c7afce

Krishnendu S. Nair, Ankita Singh . Biometric Recognition using Facial Expressions. National Technical Symposium on Advancements in Computing Technologies. NTSACT, 2 (August 2011), 1-4.

@article{
author = { Krishnendu S. Nair, Ankita Singh },
title = { Biometric Recognition using Facial Expressions },
journal = { National Technical Symposium on Advancements in Computing Technologies },
issue_date = { August 2011 },
volume = { NTSACT },
number = { 2 },
month = { August },
year = { 2011 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/ntsact/number2/3195-ntst016/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Technical Symposium on Advancements in Computing Technologies
%A Krishnendu S. Nair
%A Ankita Singh
%T Biometric Recognition using Facial Expressions
%J National Technical Symposium on Advancements in Computing Technologies
%@ 0975-8887
%V NTSACT
%N 2
%P 1-4
%D 2011
%I International Journal of Computer Applications
Abstract

Biometric recognition refers to the process of matching an input biometric to stored biometric information. In particular, biometric verification refers to matching the live biometric input from an individual to the stored biometric template about that individual. Examples of biometrics include face images, fingerprint images, iris images, retinal scans, etc. Thus, image processing techniques prove useful in the biometric recognition. In this paper, we discuss spatial frequency domain image processing methods useful for biometric recognition.

References
  1. B.V.K. Vijaya Kumar, “Tutorial survey of composite filter designs for optical correlators,” Appl. Opt. 31, pp. 4773– 4801 1992.
  2. C.F. Hester and D. Casasent, “Multivariant technique for multiclass pattern recognition,” Appl. Opt. 19, pp. 1758–1761 1980.
  3. A. Mahalanobis, B.V.K. Vijaya Kumar, and D. Casasent, “Minimum average correlation energy filters,” Appl. Opt. 26, pp. 3633-3630, 1987.
  4. Ph. Réfrégier, “Optimal trade-off filters for noise robustness, sharpness of the correlation peak, and Horner efficiency,” Opt. Lett. 16, pp. 829–831, 1991
  5. B.V.K. Vijaya Kumar and L. Hassebrook, “Performance Measures for Correlation Filters,” Applied Optics, 29, pp. 2997-3006 1990.
  6. A. Mahalanobis, B.V.K. Vijaya Kumar and S. R. F. Sims, “Distance classifier correlation filters for multi-class target recognition,” Applied Optics, 35, pp. 3127–3133, 1996.
  7. M. Savvides, B.V.K. Vijaya Kumar and P. Khosla, “Face verification using correlation filters,” Proc. Of Third IEEE Automatic Identification Advanced Technologies, Tarytown, NY, pp. 56-61, March 2002.
  8. F.J. Huang and T. Chen, "Tracking of Multiple Faces for Human-Computer Interfaces and Virtual Environments", IEEE Intl. Conf. on Multimedia and Expo., New York (2000).
Index Terms

Computer Science
Information Sciences

Keywords

FFTS DCCF SDF OTF