Abstract This paper introduces a novel system for facial landmark detection using a modified Active Appearance Model (AAM). Traditional AAMs operate directly on the pixel values of the image, leading to problems with inhomogeneously illuminated scenes. Instead of using the gray-level image to detect the facial landmark directly, the Modified Census Transformation (MCT) is performed on the region of interest (ROI) being analyzed, making the system invariant to illumination variations and nonlinear camera characteristics. To achieve efficient and robust fitting with regard to occluded or invisible parts of the face, parameter constraints, coarse to fine fitting and occlusion handling are introduced. The result shows that the new system yields good results even if some areas of the face are occluded or unrecognizable in the image.
Robust Facial Landmark Localization for Automotive Applications
2016-01-01
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Facial landmark localization , Facial features , Robust active appearance model , MCT AAM , Head-pose estimation , Driver observation , Low-resolution images , Noisy images , Modified census transformation , Inhomogeneous illuminated images Engineering , Automotive Engineering , Transportation , Transportation Technology and Traffic Engineering , Sustainable Development , Circuits and Systems
Robust windshield detection via landmark localization
Europäisches Patentamt | 2017
|ROBUST WINDSHIELD DETECTION VIA LANDMARK LOCALIZATION
Europäisches Patentamt | 2017
|Approaching human level facial landmark localization by deep learning
British Library Online Contents | 2016
|