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Symmetry 2018, 10(8), 308; https://doi.org/10.3390/sym10080308

A Coarse-to-Fine Approach for 3D Facial Landmarking by Using Deep Feature Fusion

1
School of Electrical and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2
School of Management, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Received: 11 June 2018 / Revised: 19 July 2018 / Accepted: 24 July 2018 / Published: 1 August 2018
(This article belongs to the Special Issue Deep Learning for Facial Informatics)
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Abstract

Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other kinds of facial analysis. However, most of the existing works still focus on the 2D facial image which may suffer from lighting condition variations. In order to address this limitation, this paper presents a coarse-to-fine approach to accurately and automatically locate the facial landmarks by using deep feature fusion on 3D facial geometry data. Specifically, the 3D data is converted to 2D attribute maps firstly. Then, the global estimation network is trained to predict facial landmarks roughly by feeding the fused CNN (Convolutional Neural Network) features extracted from facial attribute maps. After that, input the local fused CNN features extracted from the local patch around each landmark estimated previously, and other local models are trained separately to refine the locations. Tested on the Bosphorus and BU-3DFE datasets, the experimental results demonstrated effectiveness and accuracy of the proposed method for locating facial landmarks. Compared with existed methods, our results have achieved state-of-the-art performance. View Full-Text
Keywords: facial landmarking; 3D geometry data; 2D attribute maps; fused CNN feature; coarse-to-fine facial landmarking; 3D geometry data; 2D attribute maps; fused CNN feature; coarse-to-fine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wang, K.; Zhao, X.; Gao, W.; Zou, J. A Coarse-to-Fine Approach for 3D Facial Landmarking by Using Deep Feature Fusion. Symmetry 2018, 10, 308.

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