Comparing Landmarking Methods for Face Recognition

G.M. Beumer, Q. Tao, A.M. Bazen, Raymond N.J. Veldhuis

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

    1282 Downloads (Pure)


    Good registration (alignment to a reference) is essential for accurate face recognition. We use the locations of facial features (eyes, nose, mouth, etc) as landmarks for registration. Two landmarking methods are explored and compared: (1) the Most Likely-Landmark Locator (MLLL), based on maximizing the likelihood ratio [1], and (2) Viola-Jones detection [2]. Further, a landmark-correction method based on projection into a subspace is introduced. Both landmarking methods have been trained on the landmarked images in the BioID database [3]. The MLLL has been trained for locating 17 landmarks and the Viola-Jones method for 5 landmarks. The localization error and effects on the equal-error rate (EER) have been measured. In these experiments ground- truth data has been used as a reference. The results are described as follows: 1. The localization errors obtained on the FRGC database are 4.2, 8.6 and 4.6 pixels for the Viola-Jones, the MLLL, and the MLLL after landmark correction, respectively. The inter-eye distance of the reference face is 100 pixels. The MLLL with landmark correction scores best in the verification experiment. 2. Using more landmarks decreases the average localization error and the EER.
    Original languageUndefined
    Title of host publicationProRISC 2005, 16th Workshop on Circuits, Systems and Signal Processing
    Place of PublicationUtrecht
    Number of pages4
    ISBN (Print)90-73461-50-2
    Publication statusPublished - 17 Nov 2005
    Event16th Workshop on Circuits, Systems and Signal Processing, ProRISC 2005 - Veldhoven, Netherlands
    Duration: 17 Nov 200518 Nov 2005
    Conference number: 16


    Workshop16th Workshop on Circuits, Systems and Signal Processing, ProRISC 2005


    • SCS-Safety
    • Landmarking
    • Viola-Jones
    • landmark correction
    • EWI-13327
    • Facial feature
    • IR-59557
    • Likelihood Ratio
    • Face Recognition
    • Face Registration
    • METIS-226835

    Cite this