NDLI: Head Pose Estimation Based on Multivariate Label Distribution

Content Provider	IEEE Xplore Digital Library
Author	Xin Geng Yu Xia
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing, China (Xin Geng; Yu Xia)
Abstract	Accurate ground truth pose is essential to the training of most existing head pose estimation algorithms. However, in many cases, the "ground truth" pose is obtained in rather subjective ways, such as asking the human subjects to stare at different markers on the wall. In such case, it is better to use soft labels rather than explicit hard labels. Therefore, this paper proposes to associate a multivariate label distribution (MLD) to each image. An MLD covers a neighborhood around the original pose. Labeling the images with MLD can not only alleviate the problem of inaccurate pose labels, but also boost the training examples associated to each pose without actually increasing the total amount of training examples. Two algorithms are proposed to learn from the MLD by minimizing the weighted Jeffrey's divergence between the predicted MLD and the ground truth MLD. Experimental results show that the MLD-based methods perform significantly better than the compared state-of-the-art head pose estimation algorithms.
Starting Page	1837
Ending Page	1842
File Size	545290
Page Count	6
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.237
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation Training Face Databases Kernel Prediction algorithms
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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