• DocumentCode
    3001404
  • Title

    “Who are you?” - Learning person specific classifiers from video

  • Author

    Sivic, Josef ; Everingham, Mark ; Zisserman, Andrew

  • Author_Institution
    Lab. d´Inf., Ecole Normale Super., Paris, France
  • fYear
    2009
  • fDate
    20-25 June 2009
  • Firstpage
    1145
  • Lastpage
    1152
  • Abstract
    We investigate the problem of automatically labelling faces of characters in TV or movie material with their names, using only weak supervision from automatically-aligned subtitle and script text. Our previous work (Everingham et al. [8]) demonstrated promising results on the task, but the coverage of the method (proportion of video labelled) and generalization was limited by a restriction to frontal faces and nearest neighbour classification. In this paper we build on that method, extending the coverage greatly by the detection and recognition of characters in profile views. In addition, we make the following contributions: (i) seamless tracking, integration and recognition of profile and frontal detections, and (ii) a character specific multiple kernel classifier which is able to learn the features best able to discriminate between the characters. We report results on seven episodes of the TV series "Buffy the Vampire Slayer", demonstrating significantly increased coverage and performance with respect to previous methods on this material.
  • Keywords
    face recognition; image classification; video signal processing; frontal detections; nearest neighbour classification; specific multiple kernel classifier; video labelled proportion; Character recognition; Detectors; Face detection; Face recognition; Facial features; Kernel; Labeling; Motion pictures; TV; Web pages;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
  • Conference_Location
    Miami, FL
  • ISSN
    1063-6919
  • Print_ISBN
    978-1-4244-3992-8
  • Type

    conf

  • DOI
    10.1109/CVPR.2009.5206513
  • Filename
    5206513