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Research Article | Open Access

Joint head pose and facial landmark regression from depth images

Jie Wang1, Juyong Zhang1( ), Changwei Luo1, Falai Chen1
University of Science and Technology of China, Hefei, Anhui, 230026, China.
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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 posefacial landmarksdepth images

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Computational Visual Media
Volume 3 Issue 3,
September 2017
Pages 229-241
DOI: 10.1007/s41095-017-0082-8
Cite this article:
Wang J, Zhang J, Luo C, et al. Joint head pose and facial landmark regression from depth images. Computational Visual Media, 2017, 3(3): 229-241. https://doi.org/10.1007/s41095-017-0082-8

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Revised: 16 January 2017
Accepted: 08 March 2017
Published: 08 May 2017
© The Author(s) 2017

This article is published with open access at Springerlink.com

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