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On-line Access: 2016-06-06

Received: 2015-07-23

Revision Accepted: 2016-02-16

Crosschecked: 2016-05-06

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Gao-li Sang


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.6 P.516-526


Unseen head pose prediction using dense multivariate label distribution

Author(s):  Gao-li Sang, Hu Chen, Ge Huang, Qi-jun Zhao

Affiliation(s):  State Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu 610064, China; more

Corresponding email(s):   g.sang@foxmail.com, huchen@scu.edu.cn, 26434368@qq.com, qjzhao@scu.edu.cn

Key Words:  Head pose estimation, Dense multivariate label distribution, Sampling intervals, Inconsistent labels

Gao-li Sang, Hu Chen, Ge Huang, Qi-jun Zhao. Unseen head pose prediction using dense multivariate label distribution[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(6): 516-526.

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journal="Frontiers of Information Technology & Electronic Engineering",
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%A Ge Huang
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T1 - Unseen head pose prediction using dense multivariate label distribution
A1 - Gao-li Sang
A1 - Hu Chen
A1 - Ge Huang
A1 - Qi-jun Zhao
J0 - Frontiers of Information Technology & Electronic Engineering
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SP - 516
EP - 526
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1500235

Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation, and emotion analysis. Most existing methods estimate head poses that are included in the training data (i.e., previously seen head poses). To predict head poses that are not seen in the training data, some regression-based methods have been proposed. However, they focus on estimating continuous head pose angles, and thus do not systematically evaluate the performance on predicting unseen head poses. In this paper, we use a dense multivariate label distribution (MLD) to represent the pose angle of a face image. By incorporating both seen and unseen pose angles into MLD, the head pose predictor can estimate unseen head poses with an accuracy comparable to that of estimating seen head poses. On the Pointing’04 database, the mean absolute errors of results for yaw and pitch are 4.01° and 2.13°, respectively. In addition, experiments on the CAS-PEAL and CMU Multi-PIE databases show that the proposed dense MLD-based head pose estimation method can obtain the state-of-the-art performance when compared to some existing methods.

This paper proposes a head pose estimation method using dense multivariate label distribution. It solves the problem that the training data cannot cover all the possible test data due to large (head pose) sampling interval in training. The key idea is to produce a dense MLD to sample head pose angles densely. The results appear quite promising.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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