IS = { zkontrolovano 04 Feb 2011 },
  UPDATE  = { 2011-01-14 },
  author    = {Kalal, Zdenek and 
               Mikolajczyk, Krystian and
               Matas, Ji{\v r}{\' i} },
  title = {Face-{TLD}: Tracking-Learning-Detection Applied to Faces},
  booktitle = {17th IEEE International Conference on Image Processing ({ICIP}'2010)},
  year      = {2010},
  issn      = {1522-4880},
  venue     = {Hong Kong, China},
  day       = {26--29},
  month     = {September},
  year      = {2010},
  pages     = {3789--3792},
  book_pages = {3812},
  annote = { A novel system for long-term tracking of a human face in
   unconstrained videos is built on Tracking-Learning-Detection (TLD)
   approach. The system extends TLD with the concept of a generic
   detector and a validator which is designed for real-time face
   tracking resistent to occlusions and appearance changes. The
   off-line trained detector localizes frontal faces and the online
   trained validator decides which faces corre- spond to the tracked
   subject. Several strategies for build- ing the validator during
   tracking are quantitatively evaluated.  The system is validated on
   a sitcom episode (23 min.) and a surveillance (8 min.)  video. In
   both cases the system detects- tracks the face and automatically
   learns a multi-view model from a single frontal example and an
   unlabeled video.},
  keywords =   {tracking, learning, face detection},
  authorship = {34-33-33},
  prestige  = {important},
  publisher = {IEEE Signal Processing Society},
  address   = {445 Hoes Lane, Piscataway, USA},
  editor    = {Bonnie Law},
  project   = {GACR P103/10/1585, ICT-215078 DIPLECS only EU},
  isbn      = {978-1-4244-7994-8},