Sclera recognition using dense-SIFT

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Conference Proceeding
International Conference on Intelligent Systems Design and Applications, ISDA, 2014, pp. 74 - 79
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© 2013 IEEE. In this paper we propose a biometric sclera recognition and validation system. Here the sclera segmentation is performed bya time-adaptive active contour-based region growing technique. The sclera vessels are not prominent so image enhancement is required and hence a bank of 2D decomposition. A Haar wavelet multi-resolution filter is used to enhance the vessels pattern for better accuracy. For feature extraction, Dense Scale Invariant Feature Transform (D-SIFT) is used. D-SIFT patch descriptors of each training image are used to form bag of features by using k-means clustering and a spatial pyramid model, 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. Anencouraging Equal Error Rate (EER) of 0.66% is attained in the experiments presented.
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