• DocumentCode
    1647973
  • Title

    Are face recognition methods useful for classifying ships?

  • Author

    Harguess, Josh ; Rainey, Katie

  • Author_Institution
    Dept. of ECE, Univ. of Texas at Austin, Austin, TX, USA
  • fYear
    2011
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    Face recognition research has gained significant interest in recent years which has resulted in the development of many state-of-the-art methods. However, it is not well-known how domain specific these methods are to the problem of face recognition. Could these algorithms be used to classify and identify other objects, such as ships seen from electro-optical satellite imagery? Face recognition research shares many of the same challenges with many other types of classification research, such as illumination, pose and resolution variation. Therefore, a study of this type is warranted. We present a comparison of several classical (e.g. eigenfaces and fisherfaces) as well as recent state-of-the-art face recognition methods (e.g. sparse representation and local binary patterns) using one standard face database and two databases of ship images collected from satellite imagery. An analysis of these results as well as future directions conclude the paper.
  • Keywords
    face recognition; image classification; image representation; naval engineering computing; ships; visual databases; eigenfaces; electro-optical satellite imagery; face database; face recognition method; fisherfaces; illumination variation; local binary pattern; pose variation; resolution variation; ship classification; ship image database; sparse representation; Databases; Face; Face recognition; Feature extraction; Marine vehicles; Training; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Applied Imagery Pattern Recognition Workshop (AIPR), 2011 IEEE
  • Conference_Location
    Washington, DC
  • ISSN
    1550-5219
  • Print_ISBN
    978-1-4673-0215-9
  • Type

    conf

  • DOI
    10.1109/AIPR.2011.6176355
  • Filename
    6176355