Age prediction based on human face morphology

Age estimation is increasingly needed in numerous scientific disciplines, and thus the demand for appropriate age estimation methods is ever-growing. This master's thesis deals with age prediction based on the human facial morphology of people in the interval of 10 to 59 years. Three-dimensional virtual models of individuals of Czech, or Slovak, nationalities were used. The final sample for the thesis consists of 1046 3D facial scans, including 552 females and 494 males, for which age estimates were found using neural network models. Selected neural network models are based on two different approaches. While the PointNet, PointNet++, PointConv, and Xception networks use point clouds as input, the Multi- view Convolution Neural Network (MVCNN) utilizes multiple scan views. Point clouds were constructed from polygon meshes using uniform sampling of the mesh surface. In this case, models assess every single point. Therefore, a set containing the given object's 3D coordinates collected from its surface is obtained. Views of a particular scan result from recording a polygon mesh of the corresponding scan at a certain angle. This so-called multi-view approach is based on a projection, which records a 2D scan from various angles and then assesses and aggregates images into a general descriptor, which is..