Turkiye Klinikleri Journal of Forensic Medicine and Forensic Sciences

.: ORIGINAL RESEARCH
Investigation of the Effect of Ear Measurements on Sex Estimation in Forensic Sciences Using Machine Learning Techniques: Descriptive Research
Adli Bilimlerde Kulak Ölçümlerinin Cinsiyet Tahminine Etkisinin Makine Öğrenmesi Yöntemleri Kullanılarak İncelenmesi: Tanımlayıcı Araştırma
Serel ÖZMEN AKYOLa , Nurdan SEZGİNb
aDepartment of Computer Engineering, Kütahya University of Health Sciences Faculty of Engineering and Natural Sciences, Kütahya, Türkiye
bDepartment of Forensic Sciences, Kütahya University of Health Sciences Faculty of Engineering and Natural Sciences, Kütahya, Türkiye
Turkiye Klinikleri J Foren Sci Leg Med. 2023;20(3):161-72
doi: 10.5336/forensic.2023-96327
Article Language: EN
Full Text
ABSTRACT
 Objective: The purpose of this study is to determine how ear measurements using machine learning approaches effect sex estimation. Material and Methods: Biometric features are used in forensics to detect or verify individuals. In this study, the effect of ear measurements on one of the biometric characteristics on sex estimation was investigated. Anthropometric landmarks on the faces of 345 persons were identified for this purpose, and a data set of 36 characteristics was created by measuring distances between these landmarks. Unlike many other studies in the field of image processing, measurements obtained on biometric front face, side face, and ear images, as well as a data set containing age information, were compared in the literature, as were different machine learning approaches and accuracy rates. Results: As a result of the findings, an artificial neural networkbased sex estimate approach that appears to be consistent with the data set was developed. Two different artificial neural network models with the same structural features were built and trained. The first model uses all the data set's features as input parameters, whereas the second model does not. After ear measurements were included, the model's classification accuracy increased from 82.6% to 92.2%. Conclusion: When combined with other anthropometric parameters, ear measurements, one of the biometric characteristics, have been demonstrated to improve the success rate of sex estimation.

Keywords: Sex estimation; face biometrics; machine learning algorithms; artificial neural networks; artificial intelligence
ÖZET
 Amaç: Bu çalışmanın amacı, makine öğrenmesi yöntemleri kullanarak yapılan kulak ölçümlerinin cinsiyet tahminine ne kadar etki ettiğini incelemektir. Gereç ve Yöntemler: Adli alanda kişilerin tespit edilmesi ya da doğrulanması amacıyla biyometrik özelliklerden faydalanılmaktadır. Çalışmada, biyometrik ölçümlerden biri olan kulak ölçümlerinin cinsiyet tahminine etkisi incelenmiştir. Bu amaçla 345 kişiye ait görüntüden uzman tarafından belirlenen antropometrik noktalara göre noktalar arasındaki mesafe ölçümleri milimetrik olarak kaydedilerek 36 özniteliğe ait ölçümlerin yer aldığı bir veri seti oluşturulmuştur. Literatürde görüntü işleme alanında yapılan birçok çalışmadan farklı olarak biyometrik ölçümlerin yapılarak oluşturulduğu ön yüz, yan yüz ve kulak ölçümleri ile yaş bilgisinin yer aldığı veri seti farklı makine öğrenmesi yöntemleri uygulanarak doğruluk oranları karşılaştırılmıştır. Bulgular: Analizler sonucunda, veri setine uyumlu olduğu görülen yapay sinir ağları yöntemi ile cinsiyet sınıflandırması yapılmıştır. Aynı yapı özelliklerine sahip iki farklı yapay sinir ağı modeli oluşturularak eğitilmiştir. İlk modelde veri setinde yer alan tüm öznitelikler giriş parametresi olarak kullanılırken, ikinci modelde kulak ölçümlerine ait özniteliklere yer verilmemiştir. Kulak ölçümlerinin dâhil edilmesi ile modelin sınıflandırma doğruluk oranı %82,6'dan %92,2'ye yükselmiştir. Sonuç: Biyometrik özelliklerden biri olan kulak ölçümlerinin diğer antropometrik özellikler ile kullanıldığında kişinin cinsiyetinin tahmin edilmesinde başarı oranını artırdığı görülmüştür.

Anahtar Kelimeler: Cinsiyet tahmini; yüz biyometrisi; makine öğrenmesi algoritmaları; yapay sinir ağları; yapay zekâ
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