Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images.

K C Santosh,Raju Ranjan,Nijalingappa Pradeep,Piyush Kumar Shukla,Vikas Goel,Ekta Pandey,Stephen Jeswinde Nuagah

doi:10.1155/2022/8302674

Abstract

The use of digital medical images is increasing with advanced computational power that has immensely contributed to developing more sophisticated machine learning techniques. Determination of age and gender of individuals was manually performed by forensic experts by their professional skills, which may take a few days to generate results. A fully automated system was developed that identifies the gender of humans and age based on digital images of teeth. Since teeth are a strong and unique part of the human body that exhibits least subject to risk in natural structure and remains unchanged for a longer duration, the process of identification of gender- and age-related information from human beings is systematically carried out by analyzing OPG (orthopantomogram) images. A total of 1142 digital X-ray images of teeth were obtained from dental colleges from the population of the middle-east part of Karnataka state in India. 80% of the digital images were considered for training purposes, and the remaining 20% of teeth images were for the testing cases. The proposed gender and age determination system finds its application widely in the forensic field to predict results quickly and accurately. The prediction system was carried out using Multiclass SVM (MSVM) classifier algorithm for age estimation and LIBSVM classifier for gender prediction, and 96% of accuracy was achieved from the system.

Highlights

Technological advancement in modern medicine helps medical professionals to diagnose the nature of the medical condition of a person more effectively and medicate accurately
Age estimation and gender determination are carried out by Multiclass Support Vector Machine (SVM) (MSVM) and LIBSVM, respectively. e initial stage in the prediction model is to preprocess the input image by removing image noises, which may be adjoined while capturing images. e subsequent task of image preprocessing is to enhance the brightness and quality of the image [31]
Incisor width and intercanine distance in male teeth were found to be more compared to female teeth. e majority of all the parameters from the teeth of the male tended to be slightly more than female. e formula that was developed and used in this paper provided good and accurate results in prediction by using LIBSVM classifier and MSVM classifier. 95% of accuracy was achieved for gender determination, and 97% of accuracy was achieved for estimation of age

Summary

Introduction

Technological advancement in modern medicine helps medical professionals to diagnose the nature of the medical condition of a person more effectively and medicate accurately. E present study was conducted by using digital radiographs of teeth, known as orthopantomogram (OPG), considered as input for gender identification and age estimation of humans. It has four quadrants, upper jaw right (Q1), upper jaw left (Q2), lower jaw left (Q3), and lower jaw right (Q4). Orthopantomogram, known as OPG, and cephalogram are two different types of X-ray images in dental analysis.

Literature Review

Feature Extraction

SVM Training

Conclusion and Future Scope