Three-dimensional Scalp Shape Prediction from Face and Neck Shapes

Abstract: The scalp shape is one of the critical factors determining the proper fit for personalized helmets and other head-borne products. Three-dimensional (3D) surface scanning technology enables accurate capture of the 3D shape of an individual’s face, but most head scans do not capture the scalp shape due to hair artifacts because the typical scanning systems do not penetrate through hair. Most head shape studies use an elastic cap that compresses hair, but this does not entirely remove the effects of hair on the head surface shape. Consequently, a method to estimate scalp shape with conventional scanning would be valuable.Objective: This paper presents a model-based approach to predict the 3D scalp geometry from the rest of the head shape, i.e., face and neck, and validates the proposed method.Method: A statistical head shape model (http://humanshape.org/head/) based on 180 ethnically diverse female and male bald head scans was used in this study. A method was developed to predict scalp shapes by fitting the face and neck part of the model to target scans in the model’s shape space defined by 100 principal components. Results: New data from 81 male and female bald head scans not used in the original statistical model development were tested to validate the proposed approach. The prediction results without any information about the scalp shape showed that the mean absolute error at node locations was 2.3 mm on average, and the 95th-percentile absolute error was 6.2 mm across the test scans. When five landmark points digitized on the scalp surface were added in the fitting process, the mean absolute error was reduced to 1.7 mm with the average 95th-percentile of 4.6 mm.Discussion: Given that bald head scans are not generally available, the new method provides a useful and practical solution for obtaining scalp surface information from generic head scans for designing headgear products. The results showed that the scalp shape can be effectively predicted from face and neck shape, and the predictions can be improved through minimal information of the scalp, e.g., a few scalp landmarks or head dimensions. The major limitation of the proposed method is that errors will be larger for the scans with unusual face and neck poses. Another limitation is that although the model we used is based on an anthropometrically diverse population, more work is needed to assess the quality of the predictions for markedly different populations.

Keywords: Statistical Head Shape Model, Scalp Shape Prediction, Head under Hair, Headgear Design

DOI: 10.54941/ahfe1001893

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