Automatic frontal sinus recognition in computed tomography images for person identification

Abstract

In many cases of person identification the use of biometric features obtained from the hard tissues of the human body, such as teeth and bones, may be the only option. This paper presents a new method of person identification based on frontal sinus features, extracted from computed tomography (CT) images of the skull. In this method, the frontal sinus is automatically segmented in the CT image using an algorithm developed in this work. Next, shape features are extracted from both hemispheres of the segmented frontal sinus by using BAS (Beam Angle Statistics) method. Finally, L2 distance is used in order to recognize the frontal sinus and identify the person. The novel frontal sinus recognition method obtained 77.25% of identification accuracy when applied on a dataset composed of 310 CT images obtained from 31 people, and the automatic frontal sinus segmentation in CT images obtained a mean Cohen Kappa coefficient equal to 0.8852 when compared to the ground truth (manual segmentation).