Name
Title | ||
|---|---|---|
Down syndrome recognition using local binary patterns and statistical evaluation of the system |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.eswa.2011.01.076 | Expert Syst. Appl. |
Keywords | Field | DocType |
effective feature descriptor,down syndrome recognition,classification euclidean distance,feature extraction,syndrome people,efficient system,local binary pattern,private facial view,syndrome recognition,changed manhattan distance method,statistical evaluation,classification,challenging problem,facial feature,euclidean distance | Computer vision,Feature descriptor,Pattern recognition,Computer science,Euclidean distance,Local binary patterns,Feature extraction,Artificial intelligence,Down syndrome,Machine learning | Journal |
Volume | Issue | ISSN |
38 | 7 | Expert Systems With Applications |
Citations | PageRank | References |
28 | 1.73 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kurt Burçin | 1 | 39 | 4.17 |
| Vasif V. Nabiyev | 2 | 121 | 14.59 |