Enhancement of eye socket recognition performance using inverse histogram fusion images and the Gabor transform

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dc.authoridShehu, Harisu Abdullahi/0000-0002-9689-3290
dc.authoridBULUT, Faruk/0000-0003-2960-8725
dc.contributor.authorShehu, Harisu Abdullahi
dc.contributor.authorInce, Ibrahim Furkan
dc.contributor.authorBulut, Faruk
dc.date.accessioned2025-03-26T17:35:09Z
dc.date.available2025-03-26T17:35:09Z
dc.date.issued2025
dc.departmentİstanbul Esenyurt Üniversitesi
dc.description.abstractThe eye socket is a cavity in the skull that encloses the eyeball and its surrounding muscles. It has unique shapes in individuals. This study proposes a new recognition method that relies on the eye socket shape and region. This method involves the utilization of an inverse histogram fusion image to generate Gabor features from the identified eye socket regions. These Gabor features are subsequently transformed into Gabor images and employed for recognition by utilizing both traditional methods and deep-learning models. Four distinct benchmark datasets (Flickr30, BioID, Masked AT & T, and CK+) were used to evaluate the method's performance. These datasets encompass a range of perspectives, including variations in eye shape, covering, and angles. Experimental results and comparative studies indicate that the proposed method achieved a significantly (p<0.001) higher accuracy (average value greater than 92.18%) than that of the relevant identity recognition method and state-of-the-art deep networks (average value less than 78%). We conclude that this improved generalization has significant implications for advancing the methodologies employed for identity recognition.
dc.identifier.doi10.4218/etrij.2023-0395
dc.identifier.endpage133
dc.identifier.issn1225-6463
dc.identifier.issn2233-7326
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85205301044
dc.identifier.scopusqualityQ2
dc.identifier.startpage123
dc.identifier.urihttps://doi.org/10.4218/etrij.2023-0395
dc.identifier.urihttps://hdl.handle.net/20.500.14704/1064
dc.identifier.volume47
dc.identifier.wosWOS:001325106300001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofEtri Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250326
dc.subjectclassification; deep learning; eye socket; Gabor features; identity recognition; image matching; vector quantization
dc.titleEnhancement of eye socket recognition performance using inverse histogram fusion images and the Gabor transform
dc.typeArticle

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