Human identification using dental radiograph

Abstract

Dental biometrics is a very important feature in human identification. It can help greatly in Forensic Dentistry. In this paper, we present a method for identifying people based on shapes and appearances of their teeth using Edge detection, pixel value counting and feature extraction. This method automatically detects important features to identify a person. Wiener filter is used to reduce noise and provide a smooth image. For edge detection, we have used Canny Edge Detection algorithm where preprocessed filtered grey scale image's edge has been defined through Gaussian filtering and Edge thresholding. From the given edge detected image canny method determines the region of shape which represents binary pixel value. This pixel value can be used in image identification. Furthermore, the SURF algorithm used to define interest points. Given a query image (i.e., Postmortem radiograph), each tooth is matched with the archived teeth in the database (Antemortem radiographs). Our goal of using appearance and shape-based features together is to overcome the drawback of using only the contour of the tooth, which can be strongly affected by the quality of the images. The experimental results are based on a database of 20 panoramic x ray images show that our method is effective in identifying individuals based on their dental radiographs.