An automated method for realistic face simulation and facial landmark annotation and its application to active appearance models

Abstract

Algorithms for facial landmark detection in real-world images require manually annotated training databases. However, the task of selecting or creating the images and annotating the data is extremely time-consuming, leaving researchers with the options of investing significant amounts of time for creating annotated images optimized for the given task or resigning from creating such hand-labeled databases and to use one of the few publicly available annotated datasets with potentially limited applicability to the given problem. To allow for an alternative, we introduce a method for automatically generating realistic synthetic face images and accompanying facial landmark annotations. The proposed approach extends the automation capabilities of a commercial face modeling tool and allows large-scale generation of faces that fulfill user-defined requirements. As an additional feature, full facial landmark annotations can be computed during the generation procedure, reducing the amount of manual work required to generate a full training set to a few interactions in a graphical user interface. We describe the generation procedure in detail and demonstrate that the simulated images can be used for advanced computer vision tasks, namely training of an active appearance model that allows the detection of facial landmarks in real-world photographs.