Morphometric Analysis of 3D Soft-Tissue for Sexual Dimorphism in Human Face

被引:0
|
作者
Agbolade, Olalekan [1 ,2 ]
Nazri, Azree [1 ,2 ]
Yaakob, Razali [1 ]
Ghani, Abdul Azim [3 ]
Cheah, Yoke Kqueen [4 ]
机构
[1] Univ Putra Malaysia, Fac Comp Sci & IT, Dept Comp Sci, Seri Kembangan, Selangor, Malaysia
[2] Univ Putra Malaysia, Inst Biosci, Seri Kembangan, Selangor, Malaysia
[3] Univ Putra Malaysia, Fac Comp Sci & IT, Dept Software Engn, Seri Kembangan, Selangor, Malaysia
[4] Univ Putra Malaysia, Fac Med & Hlth Sci, Dept Biomed Sci, Seri Kembangan, Selangor, Malaysia
来源
INTERNATIONAL JOURNAL OF MORPHOLOGY | 2020年 / 38卷 / 02期
关键词
Sexual dimorphism; Facial landmark; 3D geometric morphometrics; Multi-point warping; LDA; GEOMETRIC MORPHOMETRICS; SHAPE;
D O I
暂无
中图分类号
R602 [外科病理学、解剖学]; R32 [人体形态学];
学科分类号
100101 ;
摘要
Sexual dimorphism in Homo-sapiens is a phenomenon of a direct product of evolution by natural selection where evolutionary forces acted separately on the sexes which brought about the differences in appearance between male and female such as in shape and size. Advances in morphometrics have skyrocketed the rate of research on sex differences in human and other species. However, the current challenges facing 3D in the acquisition of facial data such as lack of homology, insufficient landmarks to characterize the facial shape and complex computational process for facial point digitization require further study in the domain of sex dimorphism. This study investigates sexual dimorphism in the human face with the application of Automatic Homologous Multi-points Warping (AHMW) for 3D facial landmark by building a template mesh as a reference object which is thereby applied to each of the target mesh on Stirling/ESRC dataset containing 101 subjects (male = 47, female = 54). The semi-landmarks are subjected to sliding along tangents to the curves and surfaces until the bending energy between a template and a target form is minimal. Principal Component Analysis (PCA) is used for feature selection and the features are classified using Linear Discriminant Analysis (LDA) with an accuracy of 99.01 % which demonstrates that the method is robust.
引用
收藏
页码:367 / 373
页数:7
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