Study the effect of eye diseases on the performance of iris segmentation and recognition using transfer deep learning methods

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dc.authoridSALEH, ABBADULLAH .H/0000-0003-3019-5833
dc.contributor.authorSaleh, Abbadullah . H.
dc.contributor.authorMenemencioglu, Oguzhan
dc.date.accessioned2024-09-29T15:57:37Z
dc.date.available2024-09-29T15:57:37Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractA new deep learning-based iris recognition system is presented in the current study in the case of eye disease. Current state of art iris segmentation is either based on traditional low accuracy algorithms or heavy-weight deep-based models. In the current study segmentation section, a new iris segmentation method based on illumination correction and a modified circular Hough transform is proposed. The current method also performs a post-processing step to minimize the false positives. Besides, a ground truth of iris images is constructed to evaluate the segmentation accuracy. Many deep learning models (GoogleNet, Inception_ResNet, XceptionNet, EfficientNet, and ResNet50) are applied through the recognition step using the transfer learning approach. In the experiment part, two eye disease-based datasets are used. 684 iris images of individuals with multiple ocular diseases from the Warsaw BioBase V1 and 1,793 iris images from the Warsaw BioBase V2 are also used. The CASIA V3 Interval Iris dataset, which contains 2,639 photographs of healthy iris, is used to train deep models once, and then the transfer learning of this normal-based eye dataset is used to retrain the same deep models using Warsaw BioBase datasets. Different scenarios for training and evaluating participants are used during experiments. The trained models are evaluated using validation accuracy, training time, TPR, FNR, PPR, FDR, and test accuracy. The best accuracies are 98.5% and 97.26%, which are recorded by the ResNet50 (2-layer of transfer learning) model trained on Warsaw BioBase V1 and V2, respectively. Results indicate that the effect of eye diseases is concentrated on the segmentation phase. For recognition, no significant impact is recognized. Some disease that affects the structure (bloody eyes, trauma, iris pigment) can affect the iris recognition step partially. Our study is compared with similar studies in the case of eye diseases. The comparison proves the efficiency and high performance of the proposed methodology against all previous models on the same iris datasets.en_US
dc.identifier.doi10.1016/j.jestch.2023.101552
dc.identifier.issn2215-0986
dc.identifier.scopus2-s2.0-85174729378en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.jestch.2023.101552
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4920
dc.identifier.volume47en_US
dc.identifier.wosWOS:001105203000001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier - Division Reed Elsevier India Pvt Ltden_US
dc.relation.ispartofEngineering Science and Technology-An International Journal-Jestechen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectIris recognitionen_US
dc.subjectIris segmentationen_US
dc.subjectEye diseasesen_US
dc.subjectDeep learningen_US
dc.subjectTransfer learningen_US
dc.subjectImage processingen_US
dc.titleStudy the effect of eye diseases on the performance of iris segmentation and recognition using transfer deep learning methodsen_US
dc.typeArticleen_US

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