downsize a product designed for adults because body proportions are different especially in early childhood [18; 19]. Detailed head and facial anthropometric data of children enable designers to create products with a better fit and by doing so, increasing safety and comfort of, for example, helmets [20], pollution masks [21], oxygen masks [22] and other medical devices [23]. Furthermore, the anthropometric data of children also give insights in age related differences or growth, which is essential for designers when designing a product for children [24]. For example, [PERSON] [20] conducted a 3D anthropometric study of children (\(n=1035\)) for the development of better fitting helmets for American children aged 2 to 18. Based on the (3D) anthropometric data, he defined a new sizing system and subsequently generated head forms representing each size for helmet design. More recently, researchers have gathered 3D scans of children in order to analyse and extract shape information for the design of facial masks for both medical and commercial applications [25; 26; 27; 28]. After collecting the 3D scanned data, one of the challenges a designer encounters is to try to accommodate the variation in shape. This is especially true when developing a product that needs to closely fit a certain part of the body [29]. In order to do so, anthropometric information is translated into a sizing system and/or product dimensions. A common way of determining this is by identifying one or two key dimensions (or sizing parameters) that can be translated into product dimensions [1; 29; 30]. However, with a product that needs a close fit, such as a ventilation mask, two dimensions are not sufficient to describe the shape variation of the face [1; 30]. One way of incorporating more dimensions into the design or a sizing system is by mapping the variation of multiple key-dimensions by means of a principal component analysis (PCA) [29; 31]. PCA is a technique to increase interpretability of large datasets that consist of a high number of interrelated variables by dimensionality reduction. It generates a new set of uncorrelated (orthogonal) variables to describe the dataset while preserving as much of the variation present in the dataset. Studies have shown that a multivariate approach results in a more accurate representation of the variation compared to bivariate analysis for applications in sizing system development [1; 29; 31; 32]. Currently, PCA remains the most commonly used form of analysis of 3D anthropometric data sets. PCA has been used to study shape variation or extract relevant information for the development of fit test panels, defining sizing systems, or determining product dimensions [29; 33; 34]. In addition, PCA has been conducted in order to study body shape differences in children [35]. The input variables for a PCA can vary from measurements, landmark locations or point-cloud data/3D geometric models [1; 5; 36; 37; 11; 38]. The outcome of a PCA is not widely used in design practice because of its complexity [39], and it is not directly applicable in the design process for it does not offer an intuitive description of the shape [6]. Nevertheless, it does offer a method to present a complex and rich data set in a more understandable way. In order for the designers to be able to use this information in the design process, representative or average shape models that represent the variance of the target population are usually generated based on these analyses [5; 37]. Designers can then use these representative 3D face models to adapt their designs to the target group. In this way, protective equipment of medical products that needs to fit a children's head or face can be developed. Therefore, the aim of this study is to present and analyse the shape variation of Dutch children's faces, and, more specifically, the area relevant for the design of a ventilation mask, by conducting both a measurement based PCA and shape based PCA.