• DocumentCode
    2859573
  • Title

    Empirical Evaluation of Advanced Ear Biometrics

  • Author

    Ping Yan ; Bowyer, KevinW.

  • Author_Institution
    University of Notre Dame
  • fYear
    2005
  • fDate
    25-25 June 2005
  • Firstpage
    41
  • Lastpage
    41
  • Abstract
    We present results of the largest experimental investigation of ear biometrics to date. Approaches considered include a PCA ("eigen-ear") approach with 2D intensity images, achieving 63.8% rank-one recognition; a PCA approach with range images, achieving 55.3% Hausdorff matching of edge images from range images, achieving 67.5% and ICP matching of the 3D data, achieving 98.7%. ICP based matching not only achieves the best performance, but also shows good scalability with size of dataset. The data set used represents over 300 persons, each with images acquired on at least two different dates. In addition, the ICP-based approach is further applied on an expanded data set of 404 subjects, and achieves 97.5% rank one recognition rate. In order to test the robustness and variability of ear biometrics, ear symmetry is also investigated. In our experiments around 90% of people’s right ear and left ear are symmetric.
  • Keywords
    Bayesian methods; Biometrics; Computer science; Ear; Image recognition; Iterative closest point algorithm; Principal component analysis; Scalability; Shape; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition - Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on
  • Conference_Location
    San Diego, CA, USA
  • ISSN
    1063-6919
  • Print_ISBN
    0-7695-2372-2
  • Type

    conf

  • DOI
    10.1109/CVPR.2005.450
  • Filename
    1565339