{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:05:57Z","timestamp":1768341957387,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T00:00:00Z","timestamp":1636329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, the University of Technology Sydney, Australia and Researchers Supporting Project number RSP-2021\/14, King Saud University, Riyad","award":["2021\/14"],"award-info":[{"award-number":["2021\/14"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade\u2019s 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\u2019s 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 na\u00efve 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\u2013computer interaction and security in the cyber-physical space by improving person validation.<\/jats:p>","DOI":"10.3390\/s21217408","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T22:08:41Z","timestamp":1636409321000},"page":"7408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Detection of Iris Presentation Attacks Using Feature Fusion of Thepade\u2019s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features"],"prefix":"10.3390","volume":"21","author":[{"given":"Smita","family":"Khade","sequence":"first","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International, Deemed University, Pune 412115, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shilpa","family":"Gite","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International, Deemed University, Pune 412115, India"},{"name":"Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International, Deemed University, Pune 412115, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7809-4148","authenticated-orcid":false,"given":"Sudeep D.","family":"Thepade","sequence":"additional","affiliation":[{"name":"Pimpri Chinchwad College of Engineering, Savitribai Phule Pune University (SPPU), Pune 411044, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia"},{"name":"Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah","family":"Alamri","sequence":"additional","affiliation":[{"name":"Department of Geology & Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,8]]},"reference":[{"key":"ref_1","first-page":"74","article-title":"Advanced iris recognition using fusion techniques","volume":"13","author":"Su","year":"2019","journal-title":"NEC Tech. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lee, H., Park, S.H., Yoo, J.H., Jung, S.H., and Huh, J.H. (2020). Face recognition at a distance for a stand-alone access control system. Sensors, 20.","DOI":"10.3390\/s20030785"},{"key":"ref_3","unstructured":"Khade, S., Ahirrao, S., and Thepade, S. (2021, October 31). Bibliometric Survey on Biometric Iris Liveness Detection. 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