Pattern recognition to detect fetal alchohol syndrome using stereo facial images

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dc.contributor.advisorDouglas, Tania Sen_ZA
dc.contributor.authorVeeraragoo, Mahalingamen_ZA
dc.date.accessioned2014-07-28T18:15:43Z
dc.date.available2014-07-28T18:15:43Z
dc.date.issued2010en_ZA
dc.description.abstractFetal alcohol syndrome (FAS) is a condition which is caused by excessive consumption of alcohol by the mother during pregnancy. A FAS diagnosis depends on the presence of growth retardation, central nervous system and neurodevelopment abnormalities together with facial malformations. The main facial features which best distinguish children with and without FAS are smooth philtrum, thin upper lip and short palpebral fissures. Diagnosis of the facial phenotype associated with FAS can be done using methods such as direct facial anthropometry and photogrammetry. The project described here used information obtained from stereo facial images and applied facial shape analysis and pattern recognition to distinguish between children with FAS and control children. Other researches have reported on identifying FAS through the classification of 2D landmark coordinates and 3D landmark information in the form of Procrustes residuals. This project built on this previous work with the use of 3D information combined with texture as features for facial classification. Stereo facial images of children were used to obtain the 3D coordinates of those facial landmarks which play a role in defining the FAS facial phenotype. Two datasets were used: the first consisted of facial images of 34 children whose facial shapes had previously been analysed with respect to FAS. The second dataset consisted of a new set of images from 40 subjects. Elastic bunch graph matching was used on the frontal facial images of the study populaiii tion to obtain texture information, in the form of jets, around selected landmarks. Their 2D coordinates were also extracted during the process. Faces were classified using knearest neighbor (kNN), linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Principal component analysis was used for dimensionality reduction while classification accuracy was assessed using leave-one-out cross-validation. For dataset 1, using 2D coordinates together with texture information as features during classification produced a best classification accuracy of 72.7% with kNN, 75.8% with LDA and 78.8% with SVM. When the 2D coordinates were replaced by Procrustes residuals (which encode 3D facial shape information), the best classification accuracies were 69.7% with kNN, 81.8% with LDA and 78.6% with SVM. LDA produced the most consistent classification results. The classification accuracies for dataset 2 were lower than for dataset 1. The different conditions during data collection and the possible differences in the ethnic composition of the datasets were identified as likely causes for this decrease in classification accuracy.en_ZA
dc.identifier.apacitationVeeraragoo, M. (2010). <i>Pattern recognition to detect fetal alchohol syndrome using stereo facial images</i>. (Thesis). University of Cape Town ,Faculty of Health Sciences ,Division of Biomedical Engineering. Retrieved from http://hdl.handle.net/11427/3212en_ZA
dc.identifier.chicagocitationVeeraragoo, Mahalingam. <i>"Pattern recognition to detect fetal alchohol syndrome using stereo facial images."</i> Thesis., University of Cape Town ,Faculty of Health Sciences ,Division of Biomedical Engineering, 2010. http://hdl.handle.net/11427/3212en_ZA
dc.identifier.citationVeeraragoo, M. 2010. Pattern recognition to detect fetal alchohol syndrome using stereo facial images. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Veeraragoo, Mahalingam AB - Fetal alcohol syndrome (FAS) is a condition which is caused by excessive consumption of alcohol by the mother during pregnancy. A FAS diagnosis depends on the presence of growth retardation, central nervous system and neurodevelopment abnormalities together with facial malformations. The main facial features which best distinguish children with and without FAS are smooth philtrum, thin upper lip and short palpebral fissures. Diagnosis of the facial phenotype associated with FAS can be done using methods such as direct facial anthropometry and photogrammetry. The project described here used information obtained from stereo facial images and applied facial shape analysis and pattern recognition to distinguish between children with FAS and control children. Other researches have reported on identifying FAS through the classification of 2D landmark coordinates and 3D landmark information in the form of Procrustes residuals. This project built on this previous work with the use of 3D information combined with texture as features for facial classification. Stereo facial images of children were used to obtain the 3D coordinates of those facial landmarks which play a role in defining the FAS facial phenotype. Two datasets were used: the first consisted of facial images of 34 children whose facial shapes had previously been analysed with respect to FAS. The second dataset consisted of a new set of images from 40 subjects. Elastic bunch graph matching was used on the frontal facial images of the study populaiii tion to obtain texture information, in the form of jets, around selected landmarks. Their 2D coordinates were also extracted during the process. Faces were classified using knearest neighbor (kNN), linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Principal component analysis was used for dimensionality reduction while classification accuracy was assessed using leave-one-out cross-validation. For dataset 1, using 2D coordinates together with texture information as features during classification produced a best classification accuracy of 72.7% with kNN, 75.8% with LDA and 78.8% with SVM. When the 2D coordinates were replaced by Procrustes residuals (which encode 3D facial shape information), the best classification accuracies were 69.7% with kNN, 81.8% with LDA and 78.6% with SVM. LDA produced the most consistent classification results. The classification accuracies for dataset 2 were lower than for dataset 1. The different conditions during data collection and the possible differences in the ethnic composition of the datasets were identified as likely causes for this decrease in classification accuracy. DA - 2010 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2010 T1 - Pattern recognition to detect fetal alchohol syndrome using stereo facial images TI - Pattern recognition to detect fetal alchohol syndrome using stereo facial images UR - http://hdl.handle.net/11427/3212 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/3212
dc.identifier.vancouvercitationVeeraragoo M. Pattern recognition to detect fetal alchohol syndrome using stereo facial images. [Thesis]. University of Cape Town ,Faculty of Health Sciences ,Division of Biomedical Engineering, 2010 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/3212en_ZA
dc.language.isoeng
dc.publisher.departmentDivision of Biomedical Engineeringen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherBiomedical Engineeringen_ZA
dc.titlePattern recognition to detect fetal alchohol syndrome using stereo facial imagesen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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