Paper Info
Title
A Discriminative Approach To Frame-By-Frame Head Pose Tracking
Abstract
We present a discriminative approach to frame-by-frame head pose tracking that is robust to a wide range of illuminations and facial appearances and that is inherently immune to accuracy drift. Most previous research on head pose tracking has been validated on test datasets spanning only a small (< 20) subjects under controlled illumination conditions on continuous video sequences. In contrast, the system presented in this paper was both trained and tested on a much larger database, GENKI, spanning tens of thousands of different subjects, illuminations, and geographical locations from images on the Web. Our pose estimator achieves accuracy of 5.82 degrees, 5.65 degrees, and 2.96 degrees root-mean-square (RMS) error for yaw, pitch, and roll, respectively. A set of 4000 images from this dataset, labeled for pose, was collected and released for use by the research community.
Year
DOI
Venue
2008
10.1109/AFGR.2008.4813396
2008 8TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE & GESTURE RECOGNITION (FG 2008), VOLS 1 AND 2
Keywords
DocType
ISSN
linear regression,face recognition,face,tracking,accuracy,pose estimation,root mean square error,head,root mean square
Conference
2326-5396
Citations 
PageRank 
References 
20
1.07
15
Authors
2
Name
Order
Citations
PageRank
Jacob Whitehill198858.75
Javier R. Movellan21853150.44