Computational Visual Media

, Volume 3, Issue 3, pp 229–241 | Cite as

Joint head pose and facial landmark regression from depth images

Open Access
Research Article

Abstract

This paper presents a joint head pose and facial landmark regression method with input from depth images for realtime application. Our main contributions are: firstly, a joint optimization method to estimate head pose and facial landmarks, i.e., the pose regression result provides supervised initialization for cascaded facial landmark regression, while the regression result for the facial landmarks can also help to further refine the head pose at each stage. Secondly, we classify the head pose space into 9 sub-spaces, and then use a cascaded random forest with a global shape constraint for training facial landmarks in each specific space. This classification-guided method can effectively handle the problem of large pose changes and occlusion. Lastly, we have built a 3D face database containing 73 subjects, each with 14 expressions in various head poses. Experiments on challenging databases show our method achieves state-of-the-art performance on both head pose estimation and facial landmark regression.

Keywords

head pose facial landmarks depth images 

Notes

Acknowledgements

We thank Luo Jiang and Boyi Jiang for their help in constructing the 3DFEP database. We thank the ETHZ-Computer Vision Lab for permission to use the BIWI Kinect Head Pose database and BIWI 3D Audiovisual Corpus of Affective Communication database. This work was supported by the National Key Technologies R&D Program of China (No. 2016YFC0800501), and the National Natural Science Foundation of China (No. 61672481).

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© The Author(s) 2017

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Authors and Affiliations

  • Jie Wang
    • 1
  • Juyong Zhang
    • 1
  • Changwei Luo
    • 1
  • Falai Chen
    • 1
  1. 1.University of Science and Technology of ChinaHefei, AnhuiChina

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