Authors:
Jilliam María Díaz Barros
1
;
Frederic Garcia
2
;
Bruno Mirbach
2
;
Kiran Varanasi
3
and
Didier Stricker
3
Affiliations:
1
IEE S.A. and German Research Center for Artificial Intelligence (DFKI), Luxembourg
;
2
IEE S.A., Luxembourg
;
3
German Research Center for Artificial Intelligence (DFKI), Germany
Keyword(s):
Head Pose Estimation, Real Time, Fusion.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
;
Shape Representation and Matching
Abstract:
This paper presents a novel approach to address the head pose estimation (HPE) problem in real world and
demanding applications. We propose a new framework that combines the detection of facial landmarks with
the tracking of salient features within the head region. That is, rigid facial landmarks are detected from a given
face image, while at the same time, salient features are detected within the head region. The 3D coordinates of
both set of features result from their intersection on a simple geometric head model (e.g., cylinder or ellipsoid).
We then formulate the HPE problem as a perspective-n-point problem that we separately solve by minimizing
the reprojection error of each 3D features set and their corresponding facial or salient features in the next face
image. The resulting head pose estimations are then combined using Kalman Filter, which allows us to take
advantage of the high accuracy when using facial landmarks while enabling us to handle extreme head poses
by u
sing salient features. Results are comparable to those from the related literature, with the advantage of
being robust under real world situations that might not be covered in the evaluated datasets.
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