On the Detection and Localization of Facial Occlusions and its Use within Different Scenarios

Face analysis is a very active research field, due to its large variety of applications and the different challenges (illumination, pose, expressions or occlusions) the methods need to cope with. Facial occlusions are one of the biggest challenges since they are difficult to model and have a large influence on the performance of subsequent analysis modules. This paper describes a face detection/classification module that allows to detect and localize faces and present occlusions and discusses the use of this additional information within different application scenarios. The approach is evaluated on two databases with realistic occlusions and performs very well for the different detection/classification tasks. It achieves a f-measure of over 97% for face detection and around 86% for component detection. Regarding the occlusion detection, the proposed approach reaches a recognition rate above 91% for both faces and components.