Shape Augmented Regression Method for Face Alignment

There have been tremendous improvements of the face alignment algorithms, among which the regression framework becomes the most popular one recently. The regression based works start from an initial face shape, and they learn regression models to predict the face shape updates based on the shape-indexed local appearance features. However, most of the regression methods ignore the fact that the regression function should directly rely on the current shape (e.g. regression function for frontal face should be different from that for the left profile face). To utilize this information and improve over the existing regression based methods, we propose the shape augmented regression method for face alignment where the regression function would automatically change for different face shapes. We evaluated the performance of the proposed method on both the general "in-the-wild" database and the 300 Video in the Wild (300-VW) challenge data set. The results show that the proposed method outperforms the state-of-the-art works.

[1]  Timothy F. Cootes,et al.  Feature Detection and Tracking with Constrained Local Models , 2006, BMVC.

[2]  David J. Kriegman,et al.  Localizing parts of faces using a consensus of exemplars , 2011, CVPR.

[3]  Simon Baker,et al.  Active Appearance Models Revisited , 2004, International Journal of Computer Vision.

[4]  Luc Van Gool,et al.  Real-time facial feature detection using conditional regression forests , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[6]  Stefanos Zafeiriou,et al.  A Semi-automatic Methodology for Facial Landmark Annotation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[7]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Stefanos Zafeiriou,et al.  Incremental Face Alignment in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[11]  Simon Lucey,et al.  Deformable Model Fitting by Regularized Landmark Mean-Shift , 2010, International Journal of Computer Vision.

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

[13]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[14]  Fred Nicolls,et al.  Locating Facial Features with an Extended Active Shape Model , 2008, ECCV.

[15]  Takeo Kanade,et al.  Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

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

[17]  Maja Pantic,et al.  Facial point detection using boosted regression and graph models , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Deva Ramanan,et al.  Face detection, pose estimation, and landmark localization in the wild , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[20]  Qiang Ji,et al.  Discriminative Deep Face Shape Model for Facial Point Detection , 2015, International Journal of Computer Vision.

[21]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[22]  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).

[23]  Maja Pantic,et al.  Optimization Problems for Fast AAM Fitting in-the-Wild , 2013, 2013 IEEE International Conference on Computer Vision.

[24]  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.

[25]  Mohan M. Trivedi,et al.  Head Pose Estimation in Computer Vision: A Survey , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Zhihong Zeng,et al.  A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions , 2009, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Stefanos Zafeiriou,et al.  Offline Deformable Face Tracking in Arbitrary Videos , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[28]  Stefanos Zafeiriou,et al.  The First Facial Landmark Tracking in-the-Wild Challenge: Benchmark and Results , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).