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

Area of Science:

• Digital forensics
• Machine learning in healthcare
• Dental radiography analysis

Background:

• Manual age and gender determination by forensic experts is time-consuming.
• Advancements in computational power enable sophisticated machine learning for medical image analysis.
• Teeth provide stable, unique biological markers for identification.

Purpose of the Study:

• To develop an automated system for determining human age and gender from digital dental images.
• To leverage machine learning for rapid and accurate forensic identification.
• To analyze orthopantomogram (OPG) images for age and gender prediction.

Main Methods:

• A dataset of 1142 digital X-ray dental images from the middle-east region of Karnataka, India, was collected.
• 80% of the images were used for training and 20% for testing the developed system.
• Multiclass Support Vector Machine (MSVM) and LIBSVM classifiers were employed for age and gender prediction, respectively.

Main Results:

• The automated system achieved 96% accuracy in predicting age and gender from dental images.
• The system demonstrates high efficacy in extracting demographic information from OPGs.
• Successful application of machine learning classifiers for forensic dental analysis.

Conclusions:

• The developed automated system offers a rapid and accurate method for age and gender determination in forensic science.
• Digital dental image analysis using machine learning presents a significant advancement over manual methods.
• This technology has wide-ranging applications in forensic investigations requiring quick identification.