Feature extraction using sequential cumulative bin and overlap mean intensity for iris classification
نویسندگان
چکیده
منابع مشابه
Sequential Pattern Classification without Explicit Feature Extraction
Feature selection, representation and extraction are integral to statistical pattern recognition systems. Usually features are represented as vectors that capture expert knowledge of measurable discriminative properties of the classes to be distinguished. The feature selection process entails manual expert involvement and repeated experiments. Automatic feature selection is necessary when (i) e...
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In this paper, we evaluate the performance of feature extraction methods for iris pattern classification. Generally, the identification system using iris recognition consists of the iris localization block and the iris pattern classification block. In this paper, we used the 2D bisection-based Hough transform and the radius histogram method for the iris localization and we used multilayer perce...
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in the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. to the best of our knowledge, the effect of noise factors on feature extraction has not been c...
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ژورنال
عنوان ژورنال: TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
سال: 2018
ISSN: 1300-0632,1303-6203
DOI: 10.3906/elk-1611-297