Multi-view facial landmark detection by using a 3D shape model

Image and Vision Computing - Tập 47 - Trang 60-70 - 2016
Jan Čech1, Vojtěch Franc1, Michal Uřičář1, Jiří Matas1
1Center for Machine Perception, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic

Tài liệu tham khảo

Cristinacce, 2006, Feature detection and tracking with constrained local models, 929 Ang, 2011, Fast facial landmark detection using cascade classifiers and a simple 3D model Cootes, 2006, Active shape models — smart snakes, 929 Tzimiropoulos, 2014, Gauss-newton deformable part models for face alignment in-the-wild Gu, 2006, 3D alignment of face in a single image Amberg, 2011, Optimal landmark detection using shape models and branch and bound, 455 Felzenszwalb, 2005, Pictorial structures for object recognition, Int. J. Comput. Vis., 61, 55, 10.1023/B:VISI.0000042934.15159.49 Zhu, 2012, Face detection, pose estimation, and landmark localization in the wild, 2879 Uřičář, 2012, Detector of facial landmarks learned by the structured output SVM, 547 Cao, 2012, Face alignment by explicit shape regression X. Xiong, F. De la Torre, Supervised descent methods and its applications to face alignment, in: Proc. CVPR, 2013. Dollar, 2010, Cascaded pose regression Asthana, 2014, Incremental face alignment in the wild, 10.1109/CVPR.2014.240 Kazemi, 2014, One millisecond face alignment with an ensemble of regression trees Ren, 2014, Face alignment at 3000fps via regressing local binary features Fanelli, 2013, Real time 3D face alignment with random forests-based active appearance models Baltrusaitis, 2012, 3D constrained local model for rigid and non-rigid facial tracking Zhao, 2011, Accurate landmarking of three-dimensional facial data in the presence of facial expressions and occlusions using a three-dimensional statistical facial feature model, IEEE Trans. Syst. Man Cybern., 41, 1417, 10.1109/TSMCB.2011.2148711 Cech, 2014, A 3D approach to facial landmarks: detection, refinement, and tracking Tsochantaridis, 2005, Large margin methods for structured and interdependent output variables, J. Mach. Learn. Res., 6, 1453 Teo, 2010, Bundle methods for regularized risk minimization, J. Mach. Learn. Res., 11, 311 Nocedal, 1980, Updating quasi-newton matrices with limited storage, Math. Comput., 35, 733, 10.1090/S0025-5718-1980-0572855-7 Šochman, 2005, Waldboost — learning for time constrained sequential detection, 150 Grunert, 1841, Das pothenotische problem in erweiterter gestalt nebst über seine anwendungen in der geodäisie, Grunerts Archiv für Mathematik und Physik, Band, 1, 238 Lepetit, 2009, EPnP: an accurate O(n) solution to the PnP problem, IJCV, 81, 155, 10.1007/s11263-008-0152-6 Fischler, 1981, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Commun. ACM, 24, 381, 10.1145/358669.358692 Koestinger, 2011, Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization Snavely, 2008, Modeling the world from internet photo collections, IJCV, 80, 189, 10.1007/s11263-007-0107-3 Hartley, 2003 Gross, 2010, Multi-PIE, Image Vis. Comput., 28, 807, 10.1016/j.imavis.2009.08.002 Sagonas, 2013, A semi-automatic methodology for facial landmark annotation Sagonas, 2013, 300 faces in-the-wild challenge Uřičář, 2015, Real-time multi-view facial landmark detector learned by the structured output SVM