Age-related craniofacial differences based on spatio-temporal face image atlases

A number of studies have been developed recently in order to explore associations between craniofacial differences and genetics. Most of these works have been based on spatial face image models, adjusted for the counter effects of age. This approach provides a limited understanding of normal and abnormal craniofacial development owing to the lack of age progression information. Here, the authors propose and implement an imaging framework that combines facial landmark positioning, non-rigid registration, novel age-dependent face modelling and common distance metrics to disclose the most facial differences that vary across the time due to the subjects' age. All the experiments carried out and corresponding results presented here are based on a database comprising ordinary two-dimensional (2D) frontal face images of Down Syndrome (DS) and control sample groups. A number of craniofacial metrics have been successfully identified that highlight statistically significant and clinically relevant differences between the controls and the faces associated with DS within the age range from 1 to 18 years old, producing realistic unbiased face models with similar level of detail at all age-intervals, despite the small sample size available

Analysis of Surfaces Using Constrained Regression Models

We present a study of the relationship between the changes in the shape of the human ear due to jaw movement and acoustical feedback (AF) in hearing aids. In particular, we analyze the deformation field of the outer ear associated with the movement of the mandible (jaw bone) to understand its effect on AF and identify local regions that play a significant role. Our data contains ear impressions of 42 hearing aid users, in two different positions: open and closed mouth, and survey data including information about experienced discomfort due to AF. We use weighted support vector machines (WSVM) to investigate the separation between the presence and lack of AF and achieve classification accuracy of 80 % based on the deformation field. To robustly localize the regions of the deformation field that significantly contribute to AF we employ logistic regression penalized with elastic net (EN). By visualizing the selected variables on the mean surface, we provide clinical interpretations of the results

Analysis of Surfaces Using Constrained Regression Models

We present a study of the relationship between the changes in the shape of the human ear due to jaw movement and acoustical feedback (AF) in hearing aids. In particular, we analyze the deformation field of the outer ear associated with the movement of the mandible (jaw bone) to understand its effect on AF and identify local regions that play a significant role. Our data contains ear impressions of 42 hearing aid users, in two different positions: open and closed mouth, and survey data including information about experienced discomfort due to AF. We use weighted support vector machines (WSVM) to investigate the separation between the presence and lack of AF and achieve classification accuracy of 80 % based on the deformation field. To robustly localize the regions of the deformation field that significantly contribute to AF we employ logistic regression penalized with elastic net (EN). By visualizing the selected variables on the mean surface, we provide clinical interpretations of the results