@InProceedings{Uricar-BWILD-2015,
  IS = { zkontrolovano 26 Jun 2015 },
  UPDATE  = { 2015-06-11 },
  author =      { U{\v{r}}i{\v{c}}{\'{a}}{\v{r}}, Michal and 
		  Franc, Vojt{\v{e}}ch and 
		  Thomas, Diego and 
		  Sugimoto Akihiro and 
		  Hlav{\'{a}}{\v{c}}, V{\'{a}}clav },
  authorship =  {50-35-5-5-5},
  Affiliation = {13133-13133-NULL-NULL-13133},
  title =       {{Real-time Multi-view Facial Landmark Detector
                  Learned by the Structured Output SVM}},
  year =        {2015},
  pages =       {8},
  booktitle =   {{BWILD'15: 11th IEEE International Conference on
                  Automatic Face and Gestu re Recognition Workshops,
                  Biometrics in the Wild}},
  editor = 	{Bhanu Bir and Hadid Abdenour and Ji Qiang and Nixon
                  Mark and {\v{S}}truc Vitomir},
  publisher =   {IEEE Computer Society},
  address =     {New York, US},
  isbn =        {978-1-4799-6025-5},
  book_pages =  {60},
  month =       {May},
  day =         {8},
  venue =       {Ljubljana, Slovenia},
  organization ={COST Action IC-1106},
  annote =      {While the problem of facial landmark detection is
                  getting big attention i n the computer vision
                  community recently, most of the methods deal only
                  with near-frontal views and there is only a few
                  really multi-view detectors available, that are
                  capable of detection in a wide range of yaw angle
                  (e.g. $phi in (-90, 90)$).  We
                  describe a multi-view facial landmark detector based
                  on the Deformable Part Models, which treats the
                  problem of the simultaneous landmark detection and
                  the viewing angle estimation within a structured
                  output classification framework.  We prese nt an
                  easily extensible and flexible framework which
                  provides a real-time performance on the ``in the
                  wild'' images, evaluated on a challenging
                  ``Annotated Facial Landmarks in the Wild''
                  database. We show that our det ector achieves better
                  results than the current state of the art in terms
                  of the localization error.},
  project =     {SGS15/201/OHK3/3T/13, GACR P202/12/2071, TACR TE01020197},
  psurl       = {[Uricar-BWILD-2015.pdf]},
  www         = { http://luks.fe.uni-lj.si/bwild15/ },
  acceptance_ratio = { 0.6154 },
  keywords    = {Facial Landmark Detection, Head-pose estimation,
                  Structured Output Classi fication, Support Vector
                  Machines, Deformable Part Models},
}