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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 6/2016

01.12.2016 | Original Article

A study on the discriminating characteristics of Gabor phase-face and an improved method for face recognition

verfasst von: Iqbal Nouyed, Bruce Poon, M. Ashraful Amin, Hong Yan

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2016

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Abstract

In this paper, characteristics of different Gabor phase based facial feature representations are studied for the face recognition problem. Experiments show that filter responses acquired from larger scales show higher discriminating ability. Based on this study, following methods are proposed: (1) a faster face recognition method that is forty times faster than the conventional Gabor face representation that can discriminate faces with 91.4 % accuracy, (2) a five times faster method than the conventional representation that showed 95.7 % accuracy, (3) a novel weighted vote of Gabor filters-based face recognition method which showed 99 % accuracy, and (4) an online face authentication system using global and person specific threshold. The tests are performed using a 1200 subject dataset collected from a combination of three databases: FERET, Indian and in-house.
Vorheriger Artikel Interval valued hesitant fuzzy uncertain linguistic aggregation operators in multiple attribute decision making
Nächster Artikel Integration of data fusion and reinforcement learning techniques for the rank-aggregation problem

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Metadaten
Titel
A study on the discriminating characteristics of Gabor phase-face and an improved method for face recognition
verfasst von
Iqbal Nouyed
Bruce Poon
M. Ashraful Amin
Hong Yan
Publikationsdatum
01.12.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2016
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0440-8

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