Zhanpeng Zhang
ABSTRACT
The main challenge of facial landmark localization in real-world application is that the large changes of head pose and facial expressions cause substantial image appearance variations. To avoid high dimensional regression in the 3D and 2D facial pose spaces simultaneously, we propose a hierarchical pose regression approach, estimating the head rotation, facial components and landmarks hierarchically. The regression process works in a unified cascaded fern framework. We present generalized gradient boosted ferns (GBFs) for the regression framework, which give better performance than traditional ferns. The framework also achieves real time performance. We verify our method on the latest benchmark datasets. The results show that it outperforms state-of-the-art methods in both accuracy and speed.
- C. BenAbdelkader. Robust head pose estimation using supervised manifold learning. In ECCV, 2010. Google Scholar
Digital Library
- X. Cao, Y. Wei, F. Wen, and J. Sun. Face alignment by explicit shape regression. In CVPR, 2012. Google ScholarDigital Library
- T. Cootes, G. Edwards, and C. Taylor. Active appearance models. IEEE TPAMI, 23(6):681 --685, 2001. Google ScholarDigital Library
- M. Dantone, J. Gall, G. Fanelli, and L. Van Gool. Real-time facial feature detection using conditional regression forests. In CVPR, 2012.Google ScholarCross Ref
- P. Dollar, P. Welinder, and P. Perona. Cascaded pose regression. In CVPR, 2010.Google ScholarCross Ref
- M. Everingham, J. Sivic, and A. Zisserman. Hello! my name is... buffy-automatic naming of characters in tv video. In BMVC, 2006.Google ScholarCross Ref
- G. Fanelli, M. Dantone, J. Gall, A. Fossati, and L. Gool. Random forests for real time 3d face analysis. IJCV, 101:437--458, 2013. Google ScholarDigital Library
- J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189--1232, 2000.Google ScholarCross Ref
- G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07--49, University of Massachusetts, Amherst, 2007.Google Scholar
- O. Jesorsky, K. J. Kirchberg, and R. Frischholz. Robust face detection using the hausdorff distance. In the Third International Conference on Audio- and Video-Based Biometric Person Authentication, 2001. Google ScholarDigital Library
- M. Kostinger, P. Wohlhart, P. Roth, and H. Bischof. Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization. In ICCV Workshops, 2011.Google ScholarCross Ref
Index Terms
- Facial landmark localization based on hierarchical pose regression with cascaded random ferns
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