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2016, Volume 17, Issue 6

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Frontiers of Information Technology & Electronic Engineering >> 2016, Volume 17, Issue 6 doi: 10.1631/FITEE.1500235

Unseen head pose prediction using densemultivariate label distribution Project supported by the National Key Scientific Instrument and Equipment Development Project of China (No. 2013YQ49087903) and the National Natural Science Foundation of China (No. 61202160)

. State Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu 610064, China.. College of Mathematics and Information Engineering, Jiaxing University, Jiaxing 314001, China

Available online: 2016-06-28
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Abstract

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.

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