Linear statistical models and ridge regression used in shape index calculation on human face

0544802 - ÚI 2022 FR eng A - Abstrakt
Katina, Stanislav - Šindlář V.
Linear statistical models and ridge regression used in shape index calculation on human face.
ISCB 2021: 42nd Annual Conference of the International Society for Biostatistics: Final Programme & Book of Abstracts. Lyon: ISCB / University Lyon, 2021. s. 208-208.
[ISCB 2021: Annual Conference of the International Society for Biostatistics /42./. 18.07.2021-22.07.2021, Lyon]
Institucionální podpora: RVO:67985807

Spatial interpolation and smoothing is usually done for one surface. In our case, we have random samples of such surfaces represented by human faces captured by stereo-photogrammetry and characterised by about 150,000 points. These points are triangulated by about 300,000 triangles. The number of points is extremely high for the purpose of statistical analyses, therefore the 3D coordinates of (semi) landmarks on curves or surface patches sufficiently characterisingthe shape have to be automatically identified and this simplified model comprising about 1000 points is then used in further statistical modelling in functional data analysis setting. The identification of (semi)landmarks is a complex process during which B-splines, P-splines and thin-plate splines are used together with the measures of local surface topology, including principal curvatures and shape index. Shape index is calculated in R using different linear regression models and ridge regression model (allowing more flexibility for regression coefficients) of z coordinates on x and y coordinates, i.e. quadratic with interaction without/with intercept, cubic with interaction of x and y without/ with intercept (without/with other interactions), and similar models of higher order. The estimates of regression coefficients related to the quadratic terms and their interaction are elements of Weingarten matrix from which the principal curvatures are calculated. These models are applied on sufficiently large neighbourhood of all points in local 3D coordinate system. Since the measures of local surface topology represent principal guide in estimating locations of ridge and valley curves across the face, we aim to compare different regression models used in shape index calculation on faces of patients with facial palsy and healthy controls. We suggest to use quadratic or cubic linear regression model or ridge regression model with interaction of the first order without intercept.
Trvalý link: http://hdl.handle.net/11104/0321609