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Soft Computing

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11-03-2020 | Methodologies and Application

Computer vision algorithms for dominant contact lens feature extraction using fuzzy-logic-based classifications

Authors: S. G. Gino Sophia, V. Ceronmani Sharmila

Published in: Soft Computing | Issue 18/2020

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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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Title: Computer vision algorithms for dominant contact lens feature extraction using fuzzy-logic-based classifications
Authors: S. G. Gino Sophia
V. Ceronmani Sharmila
Publication date: 11-03-2020
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 18/2020
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-04791-1

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