Biometric identification using panoramic dental radiographic images with few-shot learning

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dc.authorid0000-0003-4094-9598en_US
dc.contributor.authorAtaş, Musa
dc.contributor.authorÖzdemir, Cüneyt
dc.contributor.authorAtaş, İsa
dc.contributor.authorAk, Burak
dc.contributor.authorÖzeroğlu, Esma
dc.date.accessioned2023-03-27T11:26:27Z
dc.date.available2023-03-27T11:26:27Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Diyarbakır Teknik Bilimler Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractDetermining identity is a crucial task especially in the cases of mass disasters such as tsunamis, earthquakes, fires, epidemics, and in forensics. Although there are various studies in the literature on biometric identification from radiographic dental images, more research is still required. In this study, a panoramic dental radiographic (PDR) image -based human identification system was developed using a customized deep convolutional neural network model in a few-shot learning scheme. The proposed model (PDR-net) was trained on 600 PDR images obtained from a total of 300 patients. As the PDR images of the patients were very different in terms of pose and intensity, they were first cropped by the domain experts according to the region of interest and adjusted to standard view with histogram equalization. A customized data augmentation approach was applied in order for the model to generalize better while it was being trained. The proposed model achieved a prediction accuracy of 84.72% and 97.91% in Rank-1 and Rank-10, respectively, by testing 144 PDR images of 72 patients that had not been previously used in training. It was concluded that well known similarity metrics such as Euclidean, Manhattan, Cosine, Pearson, Kendall's Tau and sum of absolute difference can be utilized in few-shot learning. Moreover, Cosine and Pearson similarity achieved the highest Rank 1 score of 84.72%. It was observed that as the number of rank increased, the Spearman and Kendall's Tau metrics had the same success as Cosine and Pearson. Based on the superimposed heatmap image analysis, it was determined that the maxillary, mandibular, nasal fossa, sinus and other bone forms in the mouth contributed biometric identification. It was also found that customized data augmentation parameters contributed positively to biometric identification.en_US
dc.identifier.citationAtaş, M., Özdemir, C., Ataş, İ., Ak, B. ve Özeroğlu, E. (2022). Biometric identification using panoramic dental radiographic images with few-shot learning. Turkish Journal of Electrical Engineering and Computer Sciences, 30(3), 1115-1126.en_US
dc.identifier.doi10.3906/elk-2108-61
dc.identifier.endpage1126en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85128276929
dc.identifier.scopusqualityQ2
dc.identifier.startpage1115en_US
dc.identifier.trdizinid529564
dc.identifier.urihttps://aj.tubitak.gov.tr/elektrik/issues/elk-22-30-3/elk-30-3-39-2108-61.pdf
dc.identifier.urihttps://hdl.handle.net/11468/11526
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/529564
dc.identifier.volume30en_US
dc.identifier.wosWOS:000774599800038
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorAtaş, İsa
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technical Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectFew-shot learningen_US
dc.subjectForensic informaticsen_US
dc.subjectHuman identificationen_US
dc.subjectPanoramic dental radiographsen_US
dc.titleBiometric identification using panoramic dental radiographic images with few-shot learningen_US
dc.titleBiometric identification using panoramic dental radiographic images with few-shot learning
dc.typeArticleen_US

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