Sex Determination of 3D Skull Based on a Novel Unsupervised Learning Method

In law enforcement investigation cases, sex determination from skull morphology is one of the important steps in establishing the identity of an individual from unidentified human skeleton. To our knowledge, existing studies of sex determination of the skull mostly utilize supervised learning methods to analyze and classify data and can have limitations when applied to actual cases with the absence of category labels in the skull samples or a large difference in the number of male and female samples of the skull. This paper proposes a novel approach which is based on an unsupervised classification technique in performing sex determination of the skull of Han Chinese ethnic group. The 78 landmarks on the outer surface of 3D skull models from computed tomography scans are marked, and a skull dataset of a total of 40 interlandmark measurements is constructed. A stable and efficient unsupervised algorithm which we abbreviated as MKDSIF-FCM is proposed to address the classification problem for the skull dataset. The experimental results of the adult skull suggest that the proposed MKDSIF-FCM algorithm warrants fairly high sex determination accuracy for females and males, which is 98.0% and 93.02%, respectively, and is superior to all the classification methods we attempted. As a result of its fairly high accuracy, extremely good stability, and the advantage of unsupervised learning, the proposed method is potentially applicable for forensic investigations and archaeological studies.