Abstract
Iris image recognition is an emerging approach for human identification but offers low reliability. An algorithm for dominant contact lens feature extraction based on an improved neighboring binary pattern (NBP) approach is proposed herein. Features are compared with neighboring features in various directions, assigning a value of 1 to dominant features and 0 otherwise. The features in two-dimensional binary tables are then trained using an adaptive neuro fuzzy inference system (ANFIS) and classified using various classifiers. The performance of various feature descriptors based on the classification algorithms is measured and compared using parameters such as the accuracy, training time, positive acceptance rate (PAR), and negative acceptance rate (NAR), and the PAR and NAR are compared based upon a confusion matrix of classifiers. The proposed dominant feature extraction method achieves an accuracy rate of 95.7%.
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S. G. Gino Sophia and V. Ceronmani Sharmila have no conflicts of interest related to their research work.
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(i) This article does not contain any studies with animals performed by any of the authors. (ii) The human eyes database of the Indian Institute of Delhi was used, but this did not involve humans directly.
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Appendix: Algorithm for dominant texture descriptor
Appendix: Algorithm for dominant texture descriptor
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Gino Sophia, S.G., Ceronmani Sharmila, V. Computer vision algorithms for dominant contact lens feature extraction using fuzzy-logic-based classifications. Soft Comput 24, 14235–14249 (2020). https://doi.org/10.1007/s00500-020-04791-1
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DOI: https://doi.org/10.1007/s00500-020-04791-1