Joint head pose and facial landmark regression from depth images

被引：0

作者：

Wang J. ^{[1
]}

Zhang J. ^{[1
]}

Luo C. ^{[1
]}

Chen F. ^{[1
]}

机构：

[1] University of Science and Technology of China, Hefei, Anhui

来源：

Computational Visual Media | 2017年 / 3卷 / 3期

基金：

中国国家自然科学基金;

关键词：

depth images; facial landmarks; head pose;

D O I：

10.1007/s41095-017-0082-8

中图分类号：

学科分类号：

摘要：

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. © 2017, The Author(s).

引用

页码：229 / 241

页数：12

共 40 条

[1]

Cao C., Weng Y., Lin S., Zhou K., 3D shape regression for real-time facial animation, ACM Transactions on Graphics, (2013)

[2]

Cao C., Hou Q., Zhou K., Displaced dynamic expression regression for real-time facial tracking and animation, ACM Transactions on Graphics, (2014)

[3]

Breitenstein M.D., Kuettel D., Weise T., van Gool L., Pfister H., Real-time face pose estimation from single range images, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, (2008)

[4]

Meyer G.P., Gupta S., Frosio I., Reddy D., Kautz J., Robust model-based 3D head pose estimation, Proceedings of the IEEE International Conference on Computer Vision, pp. 3649-3657, (2015)

[5]

Padeleris P., Zabulis X., Argyros A.A., Head pose estimation on depth based on particle swarm optimation, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 42-49, (2012)

[6]

Seeman E., Nickel K., Stiefelhagen R., Head pose estimation using stereo vision for human–robot interaction, Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 626-631, (2004)

[7]

Tulyakov S., Vieriu R.L., Semeniuta S., Sebe N., Robust real-time extreme head pose estimation, Proceedings of the 22nd International Conference on Pattern Recognition, pp. 2263-2268, (2014)

[8]

Burgos-Artizzu X.P., Perona P., Dollar P., Robust face landmark estimation under occlusion, Proceedings of the IEEE International Conference on Computer Vision, pp. 151-1520, (2013)

[9]

Cao X., Wei Y., Wei F., Sun J., Face alignment by explicit shape regression, International Journal of Computer Vision, 107, 2, pp. 177-190, (2014)

[10]

Dantone M., Gall J., Fanelli G., van Gool L., Real-time facial feature detection using conditional regression forests, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2578-2585, (2012)

← 1 2 3 4 →