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Cited 33 time in webofscience Cited 42 time in scopus
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Simple hybrid classifier for face recognition with adaptively generated virtual data SCIE SCOPUS

Title
Simple hybrid classifier for face recognition with adaptively generated virtual data
Authors
Ryu, YSOh, SY
Date Issued
2002-05
Publisher
ELSEVIER SCIENCE BV
Abstract
This paper presents a simple hybrid classifier for face recognition with artificially generated virtual training samples. Two sub-classifiers that work on eigenface space, use angular information obtained from training samples and the query feature point. First, training data set was expanded by adding virtual training samples generated adaptively according to the spatial distribution of each person's training samples. Second, a classifier, called the nearest feature angle (NFA) method, finds the most similar sample from an augmented training set to the query sample. Third, after finding the best matched feature line by applying the nearest feature line (ILL) method, the modified nearest feature line (MNFL) method finds the angular information between the query feature point and its projection onto best matched feature line. Finally, the hybrid classifier determines the class by comparing the angular information obtained by the two sub-classifiers. The proposed hybrid classifier exhibits an average error rate of 4.05%, which is 80.2% of that of the standard NFL method with improved robustness for different test sets of facial images. (C) 2002 Elsevier Science B.V. All rights reserved.
Keywords
classification; virtual sample; hybrid classifier; face recognition
URI
https://oasis.postech.ac.kr/handle/2014.oak/19148
DOI
10.1016/S0167-8655(01)00159-3
ISSN
0167-8655
Article Type
Article
Citation
PATTERN RECOGNITION LETTERS, vol. 23, no. 7, page. 833 - 841, 2002-05
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오세영OH, SE YOUNG
Dept of Electrical Enginrg
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