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T-norm-based classifier and FAST features for illumination invariant face recognition

T-norm-based classifier and FAST features for illumination invariant face recognition Various illumination invariant face recognition approaches have been proposed in the past. However, the issue that still remains unaddressed to a large extent is the applicability of such techniques in practical environment where computational costs and time are crucial factors in deciding the implementation and use of a face recognition system. To deal with these issues, the paper presents a technique to achieve high recognition performance using robust methodologies for feature extraction and classification which at the same time are also quick in delivering results. Features from accelerated segment test are used to identify the fiducial points. To boost the stability of the method, the conventionally used ID3 algorithm has been improved by using non-extensive entropy instead of Renyi entropy. Further, a classification method using Inner Product Classifier based on t-norms is applied which uses the errors between training features and test image features which are evaluated using the triangular/t-norms. Keywords: features from accelerated segment test; FAST; T-norm; ID3; fiducial points; frank T-norm. Reference to this paper should be made as follows: Bhat, A. (2016) ` for illumination invariant face recognition', Int. J. Biometrics, Vol. 8, Nos. 3/4, pp.301­312. Biographical notes: Aruna Bhat is a Research Scholar at http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

T-norm-based classifier and FAST features for illumination invariant face recognition

Bhat, Aruna
International Journal of Biometrics , Volume 8 (3-4) – Jan 1, 2016
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Publisher
Inderscience Publishers
Copyright
Copyright © 2016 Inderscience Enterprises Ltd.
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2016.082605
Publisher site
See Article on Publisher Site

Abstract

Various illumination invariant face recognition approaches have been proposed in the past. However, the issue that still remains unaddressed to a large extent is the applicability of such techniques in practical environment where computational costs and time are crucial factors in deciding the implementation and use of a face recognition system. To deal with these issues, the paper presents a technique to achieve high recognition performance using robust methodologies for feature extraction and classification which at the same time are also quick in delivering results. Features from accelerated segment test are used to identify the fiducial points. To boost the stability of the method, the conventionally used ID3 algorithm has been improved by using non-extensive entropy instead of Renyi entropy. Further, a classification method using Inner Product Classifier based on t-norms is applied which uses the errors between training features and test image features which are evaluated using the triangular/t-norms. Keywords: features from accelerated segment test; FAST; T-norm; ID3; fiducial points; frank T-norm. Reference to this paper should be made as follows: Bhat, A. (2016) ` for illumination invariant face recognition', Int. J. Biometrics, Vol. 8, Nos. 3/4, pp.301­312. Biographical notes: Aruna Bhat is a Research Scholar at

Journal

International Journal of Biometrics – Inderscience Publishers

Published: Jan 1, 2016

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