Individual Retrieval Based on Oral Cavity Point Cloud Data and Correntropy-Based Registration Algorithm

In this study, the authors present a novel individual retrieval method based on oral cavity point cloud data and correntropy-based registration algorithm. Since the three-dimensional oral cavity data contains a large amount of noise and outliers, it may lead to a decrease in registration accuracy, which affects the accuracy of retrieval rate. Therefore, the authors introduce the correntropy into the rigid registration algorithm to solve this problem. Then, they filter the matched point cloud data and then use the mean squared error to judge the individual differences of the model data. Finally, the accurate retrieval of the oral cavity data is realised. Experimental results demonstrate the proposed retrieval three-dimensional model algorithm can be successfully searched under different model data, which can help forensics use the characteristics of biological individuals to accurately search and identify, and improve recognition efficiency.