Three-dimensional superimposition of digital models for individual identification

Dentition is an individualizing structure in humans that may be potentially utilized in individual identification. However, research on the use of three-dimensional (3D) digital models for personal identification is rare. This study aimed to develop a method for individual identification based on a 3D image registration algorithm and assess its feasibility in practice. Twenty-eight college students were recruited; for each subject, a dental cast and an intraoral scan were taken at different time points, and digital models were acquired. The digital models of the dental casts and intraoral scans were assumed as antemortem and postmortem dentition, respectively. Additional 72 dental casts were extracted from a hospital database as a suspect pool together with 28 antemortem models. The dentition images of all of the models were extracted. Correntropy was introduced into the traditional iterative closest point algorithm to compare each postmortem 3D dentition with 3D dentitions in the suspect pool. Point-to-point root mean square (RMS) distances were calculated, and then 28 matches and 2772 mismatches were obtained. Statistical analysis was performed using the Mann-Whitney U test, which showed significant differences in RMS between matches (0.18 ± 0.03 mm) and mismatches (1.04 ± 0.67 mm) (P < 0.05). All of the RMS values of the matched models were below 0.27 mm. The percentage of accurate identification reached 100% in the present study. These results indicate that this method for individual identification based on 3D superimposition of digital models is effective in personal identification.