A parametric representation of the complex biological structure of the human pinna is of considerable interest in applications such as the design of ear prostheses, personalization of spatial hearing, and biometric identification. Here, we describe BezierPPM, a parametric pinna model with parameters closely linked to human ear structures. BezierPPM represents the ear geometry as cubic Beziér curves and includes local modifiers of predefined concave areas. We evaluated BezierPPM by manually registering its parameters to 20 ears selected from various databases of digitized ears. The root-mean-square error between the registered and target geometries was on average 1.5 ± 0.2mm, and the 2-mm and 1-mm completeness was 87 ± 4% and 66 ± 5%, respectively. This indicates that BezierPPM was capable of representing human pinnae very well. An in-depth analysis showed that most of the inaccuracies were located on the back side of the ear, an area having a rather low relevance for most target applications. To address potential future applications in machine-learning settings, we used BezierPPM to create a database of synthetic pinna geometries and trained a toy-example neural network to estimate BezierPPM parameters from multi-view images of this database. The estimated parameters resulted in estimated pinna geometries with a mean error of 0.3mm, indicating that BezierPPM can accurately describe a wide variety of human pinnae, also in machine-learning settings. In summary, our evaluations demonstrate a high potential of BezierPPM for applications requiring a good representation of the relevant parts of pinna geometry, especially when targeting applications in machine-learning settings.