Authors:
Marcin Kopaczka
;
Kemal Acar
and
Dorit Merhof
Affiliation:
RWTH Aachen University, Germany
Keyword(s):
Thermal Infrared, Face Tracking, Facial Landmark Detection, Active Appearance Model.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Human and Computer Interaction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Shape Representation and Matching
;
Tracking and Visual Navigation
Abstract:
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 s
how that AAMs are a robust method for the detection and tracking of
facial landmarks in the LWIR spectrum.
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