Automatic detailed localization of facial features
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
A pose-independent method for 3D face landmark formalization
Computer Methods and Programs in Biomedicine
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Dysmorphology refers to study of human congenital malformations (birth defects). Most of the case reporting in dysmorphology is subjective and is based on experience of the reporting clinician. We have used the methods of geometric morphometrics to analyze the variation in faces of normal individuals and those with dysmorphic syndrome. We obtained photographs of 20 individuals with Rubinstein Taybi syndrome and 30 normal, age and sex matched individuals. The photographs were digitized with 16 landmarks on the face to obtain 32 ''x'' and ''y'' co-ordinates. These co-ordinates were then subjected to generalized procrustes superimposition in order to normalize for effects of size, rotation and position of image. The procrustes residuals thus obtained were then subjected to principal component analysis. The principal component analysis resulted in extraction of three important principal components explaining 41%, 17% and 14% of variance, respectively. Discriminant analysis could differentiate the two groups using first two principal component scores for each individual, with a predictive accuracy of 76% (Wilks @l=0.725, @g^2=15.09, d.f.=2, p=0.001). Binary logistic regression analysis showed predictive accuracy of 78% based on this model. The utility of the subjective evaluation of facial characteristics is multifold. The results of the analysis can be used as representatives of the facial dysmorphism for any genotype-phenotype association study. We conclude that application of the principles of geometric morphometrics to study of shape variation in facies of patients with dysmorphic syndromes appears to be a promising new area of research.