Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model

Xiang Yu, Junzhou Huang, Shaoting Zhang, Wang Yan, Dimitris N. Metaxas; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 1944-1951

Abstract


This paper addresses the problem of facial landmark localization and tracking from a single camera. We present a two-stage cascaded deformable shape model to effectively and efficiently localize facial landmarks with large head pose variations. For face detection, we propose a group sparse learning method to automatically select the most salient facial landmarks. By introducing 3D face shape model, we use procrustes analysis to achieve pose-free facial landmark initialization. For deformation, the first step uses mean-shift local search with constrained local model to rapidly approach the global optimum. The second step uses component-wise active contours to discriminatively refine the subtle shape variation. Our framework can simultaneously handle face detection, pose-free landmark localization and tracking in real time. Extensive experiments are conducted on both laboratory environmental face databases and face-in-the-wild databases. All results demonstrate that our approach has certain advantages over state-of-theart methods in handling pose variations 1 .

Related Material


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[bibtex]
@InProceedings{Yu_2013_ICCV,
author = {Yu, Xiang and Huang, Junzhou and Zhang, Shaoting and Yan, Wang and Metaxas, Dimitris N.},
title = {Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}