Human identification with dental panoramic radiographic images

Abstract

Dental panoramic radiographic images are commonly used as biometrics for human identification. In this study, a novel method is presented for identifying humans by matching 2D panoramic dental X-ray images. Each tooth is first identified and labeled with support vector machines and probabilistic graphical models. Missing teeth and dental restorations are also detected. Then, matching scores between images are calculated according to tooth-wise appearance and geometric similarities by taking dental restorations into account. The method is tested on a dataset including 206 dental panoramic X-ray images of 170 different subjects. The proposed method has 81% rank-1 accuracy and 89% rank-2 accuracy.

Dental Kişi Tanıma Amacıyla Panoramik Röntgen Görüntülerinde Yeni Bir Çene Bölütleme Yöntemi

“…Kişi tanıma amacıyla dental radyografi segmentasyonu için literatürde çeşitli yöntemler bulunmaktadır. Bu çalışmalarda graf tabanlı (Barboza ve ark., 2012, Nomir ve ark., 2012), yarı otomatik (Jain ve ark., 2003, Bozkurt ve Karagöl, 2020, eşikleme (Abdel-Mottaleb ve ark., 2003), parlaklık izdüşümü (Jain ve Chen, 2004, Frejlichowski ve Wanat, 2011b, Oktay 2017, Ølberg ve Goodwin, 2016, aktif kontur modeli (Chen ve Jain 2004), bağlantılı bileşenler (Said ve ark., 2006), havza (Frejlichowski ve Wanat, 2011a), PHOG (Oktay,2017), maske (Dibeh ve ark. 2018), PSO (Bozkurt ve Karagöl, 2020) gibi yöntemlerden faydalanılmıştır.…”

Cadaver identification with dental radiographs using isoperimetric and nodal graph approach

“…Human identification with dental panoramic radiographs is a challenging task. Few of the researchers [19, 20] were conducted their experiment with these radiographs. Dental restorations were also detected by AyseBetulOktay.…”

Automatic human identification based on dental X-ray radiographs using computer vision

“…For the matching process, the tooth appearance (similarity of the descriptors of two data sets) and the distance to the middle of mouth gap were compared. In all methods, high identification success rates of 83% (Zhou et al 9 ), 86% (Hofer et al 10 ), 55% (Barboza et al 11 ), 67% (Frejlichowski et al 12 ), 72% (Karunya et al 13 ) and 81% (Oktay 14 ) were achieved; however, for the evaluation the number of DPRs and persons was very low (maximum 206 DPRs of 170 persons). A further restriction is that optimal conditions are required for the application of the methods currently described, i.e.…”

“…The recent literature [9][10][11][12][13][14] reports different approaches and algorithms for identifying individuals from DPRs. Most of the methods currently described extract the tooth contours 9,[11][12][13] and/or dental works 10,13 , i.e.…”

“…For the contour comparison she used two different methods, the so-called Shape Context 15 and the Beam Angle Statistics 16 . Oktay 14 identified the position of each tooth using machine learning and graphical models. Furthermore, a descriptor of the Pyramidal Histogram of Oriented Gradients (PHOG) 17 describes each tooth.…”