Gender Determination from Teeth Images via Hybrid Feature Extraction Method

Abstract

Teeth are a significant resource for determining the features of an unknown person, and gender is one of the important pieces of demographic information. For this reason, gender analysis from teeth is a current topic of research. Previous literature on gender determination have generally used values obtained through manual measurements of the teeth, gingiva, and lip area. However, such methods require extra effort and time. Furthermore, since sexual dimorphism varies among populations, it is necessary to know the optimum values for each population. This study uses a hybrid feature extraction method and a Support Vector Machine (SVM) for gender determination from teeth images. The study group was composed of 60 Turkish individuals (30 female, 30 male) between the ages of 19 and 27. Features were automatically extracted from the intraoral images through a hybrid method that combines two-dimensional Discrete Wavelet Transformation (DWT) and Principle Component Analysis (PCA). Classification was performed from these features through SVM. The system can be easily used on any population and can perform fast and low-cost gender determination without requiring any extra effort.