Down syndrome recognition using local binary patterns and statistical evaluation of the system
Down syndrome has a private facial view, thus it can be recognized by using facial features. But this is a very challenging problem when the similarity between the faces of people with Down syndrome and not Down syndrome people are considered. Therefore, we used the local binary pattern (LBP) approa...
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Published in | Expert systems with applications Vol. 38; no. 7; pp. 8690 - 8695 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.07.2011
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Subjects | |
Online Access | Get full text |
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Summary: | Down syndrome has a private facial view, thus it can be recognized by using facial features. But this is a very challenging problem when the similarity between the faces of people with Down syndrome and not Down syndrome people are considered. Therefore, we used the local binary pattern (LBP) approach for feature extraction which is a very effective feature descriptor. For classification Euclidean distance and Changed Manhattan distance methods are used. In this way, we improved an efficient system to recognize Down syndrome. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 |
ISSN: | 0957-4174 1873-6793 |
DOI: | 10.1016/j.eswa.2011.01.076 |