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dc.contributor.advisorHamerly, Gregory James, 1977-
dc.contributor.authorChiam, Tak Chien.
dc.date.accessioned2012-11-29T16:27:46Z
dc.date.available2012-11-29T16:27:46Z
dc.date.copyright2012-08
dc.date.issued2012-11-29
dc.identifier.urihttp://hdl.handle.net/2104/8526
dc.description.abstractFacial age estimation from images is a difficult problem, both because it is naturally difficult to tell the exact age of a person visually, and because of the variations in images, such as illumination, pose, and expression. We want to classify people into two groups, children (age ≤ 5) and adults (age > 5), to facilitate the detection of retinoblastoma, a type of pediatric cancer. Current regression based methods are ineffective, as they usually have mean absolute error of 5 years, which is too high for our purposes. We study the facial anthropometric measurements of humans at different ages, and build a system based on these growth patterns. We detect 76 facial landmarks using Active Shape Models, analyze all possible ratios computable from these landmarks, and use the best ratios as input into a Support Vector Machine. Our final system does very well on our problem, correctly classifying 85% of images.en_US
dc.language.isoen_USen_US
dc.publisheren
dc.rightsBaylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission.en_US
dc.subjectAge estimation.en_US
dc.subjectAge classification.en_US
dc.subjectLeukocoria detection.en_US
dc.titleAge classification from facial images for detecting retinoblastoma.en_US
dc.typeThesisen_US
dc.description.degreeM.S.en_US
dc.rights.accessrightsWorldwide access.en_US
dc.rights.accessrightsAccess changed 1/29/15.
dc.contributor.departmentComputer Science.en_US
dc.contributor.schoolsBaylor University. Dept. of Computer Science.en_US


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