{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ensemble-of-multi-view-learning-classifiers","title":"Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection","arxiv_id":"1811.10068","date":"2018-11-25","proceeding":null,"authors":["Andrey Kuehlkamp","Allan Pinto","Anderson Rocha","Kevin Bowyer","Adam Czajka"],"abstract":"The adoption of large-scale iris recognition systems around the world has\nbrought to light the importance of detecting presentation attack images\n(textured contact lenses and printouts). This work presents a new approach in\niris Presentation Attack Detection (PAD), by exploring combinations of\nConvolutional Neural Networks (CNNs) and transformed input spaces through\nbinarized statistical image features (BSIF). Our method combines lightweight\nCNNs to classify multiple BSIF views of the input image. Following explorations\non complementary input spaces leading to more discriminative features to detect\npresentation attacks, we also propose an algorithm to select the best (and most\ndiscriminative) predictors for the task at hand.An ensemble of predictors makes\nuse of their expected individual performances to aggregate their results into a\nfinal prediction. Results show that this technique improves on the current\nstate of the art in iris PAD, outperforming the winner of LivDet-Iris2017\ncompetition both for intra- and cross-dataset scenarios, and illustrating the\nvery difficult nature of the cross-dataset scenario.","url_abs":"http://arxiv.org/abs/1811.10068v1","url_pdf":"http://arxiv.org/pdf/1811.10068v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ensemble-of-multi-view-learning-classifiers","repo_url":"https://github.com/akuehlka/emvlc-ipad","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cross-domain-iris-presentation-attack","task_name":"Cross-Domain Iris Presentation Attack Detection"},{"task_slug":"iris-recognition","task_name":"Iris Recognition"},{"task_slug":"multi-view-learning","task_name":"MULTI-VIEW LEARNING"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}