Facial Landmark Detection Under Large Pose

Facial landmark detection is a necessary step in many vision tasks and plenty of excellent methods have been proposed to solve this problem. However, for the conditions with large pose and complex expression, these works usually suffer an eclipse. In this paper, we propose a two-stage cascade regression framework using patch-difference features to overcome the above problem. In the first stage, by applying the patch-difference feature and augmenting the large pose samples to the classical shape regression model, salient landmarks (eye centers, nose, mouth corners) can be located precisely. In the second stage, by applying enhanced feature section constraint to the patch-difference feature, multi-landmark detection is achieved. Experimental results show that our algorithm has a significant improvement compared to the classical shape regression method and achieves superior results on COFW dataset.

[1]  Stefanos Zafeiriou,et al.  Robust Discriminative Response Map Fitting with Constrained Local Models , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Georgios Tzimiropoulos,et al.  Project-Out Cascaded Regression with an application to face alignment , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[4]  Arun Ross,et al.  Automatic facial makeup detection with application in face recognition , 2013, 2013 International Conference on Biometrics (ICB).

[5]  Fernando De la Torre,et al.  Supervised Descent Method and Its Applications to Face Alignment , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Shiguang Shan,et al.  Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment , 2014, ECCV.

[8]  Yuning Jiang,et al.  Extensive Facial Landmark Localization with Coarse-to-Fine Convolutional Network Cascade , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[9]  Sina Honari,et al.  Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Pietro Perona,et al.  Cascaded pose regression , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[12]  Xiaogang Wang,et al.  Deep Convolutional Network Cascade for Facial Point Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  David J. Kriegman,et al.  Localizing Parts of Faces Using a Consensus of Exemplars , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Cheng Li,et al.  Face alignment by coarse-to-fine shape searching , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Donghoon Lee,et al.  Face alignment using cascade Gaussian process regression trees , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Ashraf A. Kassim,et al.  Recurrent 3D-2D Dual Learning for Large-Pose Facial Landmark Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[17]  Hanjiang Lai,et al.  Robust Facial Landmark Detection via Recurrent Attentive-Refinement Networks , 2016, ECCV.

[18]  Jian Sun,et al.  Face Alignment at 3000 FPS via Regressing Local Binary Features , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Oksam Chae,et al.  Local Directional Number Pattern for Face Analysis: Face and Expression Recognition , 2013, IEEE Transactions on Image Processing.

[20]  Thomas S. Huang,et al.  Interactive Facial Feature Localization , 2012, ECCV.

[21]  Pietro Perona,et al.  Robust Face Landmark Estimation under Occlusion , 2013, 2013 IEEE International Conference on Computer Vision.

[22]  Fernando De la Torre,et al.  Global supervised descent method , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Xiaoou Tang,et al.  Learning Deep Representation for Face Alignment with Auxiliary Attributes , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  George Trigeorgis,et al.  Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Jian Sun,et al.  Face Alignment by Explicit Shape Regression , 2012, International Journal of Computer Vision.

[26]  Dong Guo,et al.  Digital face makeup by example , 2009, CVPR.