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  • A New Efficient and Adaptive Sclera Recognition System

    Author(s)
    Das, Abhijit
    Pal, Umapada
    Ferrer Ballester, Miguel Angel
    Blumenstein, Michael
    Griffith University Author(s)
    Blumenstein, Michael M.
    Das, Abhijit
    Year published
    2014
    Metadata
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    Abstract
    In this paper an efficient and adaptive biometric sclera recognition and verification system is proposed. Sclera segmentation was performed by Fuzzy C-means clustering. Since the sclera vessels are not prominent, in order to make them clearly visible image enhancement was required. Adaptive histogram equalization, followed by a bank of Discrete Meyer Wavelet was used to enhance the sclera vessel patterns. Feature extraction was performed by, Dense Local Directional Pattern (D-LDP). D-LDP patch descriptors of each training image are used to form a bag of features; further Spatial Pyramid Matching was used to produce the final ...
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    In this paper an efficient and adaptive biometric sclera recognition and verification system is proposed. Sclera segmentation was performed by Fuzzy C-means clustering. Since the sclera vessels are not prominent, in order to make them clearly visible image enhancement was required. Adaptive histogram equalization, followed by a bank of Discrete Meyer Wavelet was used to enhance the sclera vessel patterns. Feature extraction was performed by, Dense Local Directional Pattern (D-LDP). D-LDP patch descriptors of each training image are used to form a bag of features; further Spatial Pyramid Matching was used to produce the final training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset was used here for experimentation of the proposed system. To investigate regarding sclera patterns adaptively with respect to change in environmental condition, population, data accruing technique and time span two different session of the mention dataset are utilized. The images in two sessions are different in acquiring technique, representation, number of individual and they were captured in a gap of two weeks. An encouraging Equal Error Rate (EER) of 3.95% was achieved in the above mention investigation.
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    Conference Title
    Computational Intelligence in Biometrics and Identity Management (CIBIM), 2014 IEEE Symposium on
    Publisher URI
    http://ieee-ssci.org/CIBIM.html
    DOI
    https://doi.org/10.1109/CIBIM.2014.7015436
    Subject
    Image Processing
    Publication URI
    http://hdl.handle.net/10072/67902
    Collection
    • Conference outputs

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