## 4 Discussion
When determining the number of relevant principle components, a selection of components needs to be made that explain a cumulative percentage of the population. In this study, these seven PC's explain 91% of the total variation and, for most applications, a threshold between 70% and 90% is considered sufficient [32; 47]. The shape variation along each PC is visualized through the shape modes that were generated for the average, +3 and \(-\)3 standard deviations. Similar to the measurement based PCA, the first PC describes the variation in the overall size of the face and the second PC describes the variation in the width of the face that resembles the results of the shape based PCA conducted by [PERSON] et al. [37] and [PERSON] et al. [5] for their study of face shape variations of U.S. civilian workers and Chinese adults, respectively. The shape variations in the remaining PC's are more subtle and contribute less to the overall variation. Apart from the shape modes that are previously presented in Figures 6 and 7, and in order to illustrate the face variation of real participants, Figure 8 shows the measurement-based PC scatterplot with a preliminary selection of 3D face scans of eight participants. A selection was made of five faces that were distributed among the PC 1-axis, including a small (A), close to average (F) and a large (I) face. Secondly, for B and C, two additional faces were selected to illustrate the differences along the PC2 axis. Next, to illustrate the variation in shape and the possible implications for the design of a ventilation mask, contours were projected on each of these faces (Rhinoceros(r)). Each contour passes the sellion, pogonion and approximately 10 mm distance from the left and right cheillion landmarks. They were then aligned at the sellion landmark. These points were selected  
Figure 8: Scatterplot of the measurements based principal component scores including examples of different face shapes. because they could represent the preliminary contour of the rim of the ventilation mask. This illustration shows that the contours indeed not only vary in size but also in shape (Figure 9). When analysing the contours from the front, we observe that, as the contour size increases, the shape varies but this does not necessarily scale proportionally. From the side, we observe that, as the contour increases in height, the depth of the contour also increases. This suggests that, when developing ventilation masks for children, one should take into account the facial characteristics as well as the size in order to achieve a good fit--for instance, by using a parametric design that is adjusted to different face shapes rather than simply scaling a product to different sizes. Both the dimension and shape based PCA give insight into how the face shape of Dutch children of different age groups varies. Each method reveals this information concerning the variation in face shapes in a different way. The dimension based PCA gives insight into which product relevant dimension influences the variation of face shape and to which extent. This helps the designer to understand the relationship between the product relevant dimensions and the face shape variation of the children in the dataset. The shape based PCA helps the designer to understand shape variation in a more visual way and shows how the face-scapes vary. The shape modes that are generated based on the analysis can facilitate the design of face related products in a Computer Aided Design (CAD) environment. For a designer of head and face related products, both analysis methods can be useful and are complementary because they facilitate in organising and presenting the complex 3D data. However, in order for the designer to be able to conduct a PCA and get the most out of the data, they need advanced knowledge of statistics, the ability to apply custom algorithms for data processing and to translate the results of these analysis to generate shape modes and specific 3D modelling skills to be able to visualize this data.