Robust Facial Landmark Detection and Face Tracking in Thermal Infrared Images using Active Appearance Models

Long wave infrared (LWIR) imaging is an imaging modality currently gaining increasing attention. Facial images acquired with LWIR sensors can be used for illumination invariant person recognition and the contactless extraction of vital signs such as respiratory rate. In order to work properly, these applications require a precise detection of faces and regions of interest such as eyes or nose. Most current facial landmark detectors in the LWIR spectrum localize single salient facial regions by thresholding. These approaches are not robust against out-of-plane rotation and occlusion. To address this problem, we therefore introduce a LWIR face tracking method based on an active appearance model (AAM). The model is trained with a manually annotated database of thermal face images. Additionally, we evaluate the effect of different methods for AAM generation and image preprocessing on the fitting performance. The method is evaluated on a set of still images and a video sequence. Results show that AAMs are a robust method for the detection and tracking of facial landmarks in the LWIR spectrum.