Fuzzy Logic Based Sclera Recognition

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Author(s)
Das, Abhijit
Pal, Umapada
Ballester, Miguel Angel Ferrer
Blumenstein, Michael Myer
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Dimitar P. Filev

Date
2014
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Beijing, China

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Abstract

In this paper a sclera recognition and validation system is proposed. Here sclera segmentation was performed by Fuzzy logic-based clustering. Since the selera vessels are not prominent, image enhancement was required. A Fuzzy logic-based Brightness Preserving Dynamic Fuzzy Histogram Equalization and discrete Meyer wavelet was used to enhance the vessel patterns. For feature extraction, the Dense Local Binary Pattern (D-LBP) was used. D-LBP patch descriptors of each training image are used to form a bag of features, which is used to produce the training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset is used here for experimentation. An encouraging Equal Error Rate (EER) of 4.31% was achieved in our experiments.

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Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE2014

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Artificial intelligence not elsewhere classified

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