Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features

Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper p...

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Autores principales:	Smita Khade, Shilpa Gite, Sudeep D. Thepade, Biswajeet Pradhan, Abdullah Alamri
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	iris images liveness detection TSBTC GLCM machine learning feature extraction Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ce66a80ecb1c4e7596ebfe8428bd4fa7
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spelling	oai:doaj.org-article:ce66a80ecb1c4e7596ebfe8428bd4fa72021-11-11T19:19:47ZDetection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features10.3390/s212174081424-8220https://doaj.org/article/ce66a80ecb1c4e7596ebfe8428bd4fa72021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7408https://doaj.org/toc/1424-8220Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade’s sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade’s SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade’s SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human–computer interaction and security in the cyber-physical space by improving person validation.Smita KhadeShilpa GiteSudeep D. ThepadeBiswajeet PradhanAbdullah AlamriMDPI AGarticleiris imagesliveness detectionTSBTCGLCMmachine learningfeature extractionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7408, p 7408 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	iris images liveness detection TSBTC GLCM machine learning feature extraction Chemical technology TP1-1185
spellingShingle	iris images liveness detection TSBTC GLCM machine learning feature extraction Chemical technology TP1-1185 Smita Khade Shilpa Gite Sudeep D. Thepade Biswajeet Pradhan Abdullah Alamri Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features
description	Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade’s sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade’s SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade’s SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human–computer interaction and security in the cyber-physical space by improving person validation.
format	article
author	Smita Khade Shilpa Gite Sudeep D. Thepade Biswajeet Pradhan Abdullah Alamri
author_facet	Smita Khade Shilpa Gite Sudeep D. Thepade Biswajeet Pradhan Abdullah Alamri
author_sort	Smita Khade
title	Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features
title_short	Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features
title_full	Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features
title_fullStr	Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features
title_full_unstemmed	Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features
title_sort	detection of iris presentation attacks using feature fusion of thepade’s sorted block truncation coding with gray-level co-occurrence matrix features
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ce66a80ecb1c4e7596ebfe8428bd4fa7
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Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features

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