## 4 Discussion  
3D anthropometric data of children's faces are necessary in order to develop head and face gear with a proper fit. However, detailed (3D) anthropometric data of children's heads and faces are still lacking, even though there is a clear demand from the industry [45]. The aim of this study was to map the variation of the size and shape of children's faces, with a focus on the area relevant to mask design, by a multivariate and shape-based approach. An independent \(t\)-test was conducted in order to compare differences between gender, and the results showed that only 20 out of the 252 comparisons were considered significantly different, and these differences appeared to be distributed among the dimensions. When considering anthropometric data of children of a certain age range for applications in product design, genders can often be combined because of the greater variability of different dimensions compared to the variability due to gender [20]. Therefore, the PCA was conducted without differentiating between gender. In addition, the children's dataset was not classified in age groups for the PCA. When designing a product for children within a certain age range, it is often more appropriate to investigate the variability of relevant dimensions irrespective of age [24; 46]. There is a large variation in size amongst children within the same age group, which results in an overlap between successive age groups for most of the body dimensions [28]. In addition, the mean values and standard deviations of these body dimensions increase with age, which indicates an increasing differentiation among age groups. The differences in body shape between children of the same age will become larger and larger [46]. Indeed, the scatterplot of the PC scores (Figure 8) shows that colours representing each age group are scattered throughout the graph, which illustrates the overlap between different age groups for multiple face dimensions. A PCA was conducted on a selection of relevant dimensions for mask design, in an attempt to describe the morphological trend in the dataset to be used in the development of a ventilation mask. The result of the PCA describes the morphological distribution of the children's faces over an age span of 0.5 to 7 years. The first principal component describes the variation in the overall size of the face. While the PC1 score increases, the overall size of the face also increases. The second component describes the width of the face. Children with a relatively high PC 2 score have short and broad faces, whereas children with a low PC 2 score have longer and narrower faces. The findings of this study are in agreement with the study of [PERSON] et al. [26] who studied facial dimensions of Korean children for respirator  
Figure 7: Scatterplot of shape based PCA including shape modes (\(-1.5\) and \(+1.5\) standard deviations). design, where face length and face width were also found to influence the variation the strongest. Interestingly, [PERSON] et al. [31] use a similar description for the size categories of adults based on a PCA as part of his study for the development of a new fit test panel for respirators--namely, small, medium, large, short-wide and long-narrow. This suggests that the overall characteristics that describe the face variation are similar for adults; however, there is one clear difference. The relatively high scores for PC 1 in this dataset (0.623-0.907) compared to the PC 1 scores for adults (0.194-0.426) indicate that the overall size of the face varies more in children. This can be due to age differences, resulting in a relatively larger difference between the smallest face and the largest face in the children's dataset. A shape based PCA offers an even more detailed way of investigating face shapes. By conducting a PCA on the 3D location of the vertices of each 3D face area, the face shape variation of the dataset could be revealed. In this study, the first seven PC's were selected in order to investigate the shape variation.