3-D Oral Shape Retrieval Using Registration Algorithm

Abstract

In this paper, we present a novel 3-D oral shape retrieval using correntropy-based registration algorithm. Fast matching as the traditional registration method can achieve, its registration accuracy is disturbed by noise and outliers. Since the 3-D oral model 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, we introduce the correntropy into the rigid registration algorithm to solve this problem. Although the noise and outliers are suppressed by the correntropy-based algorithm, these noises and outliers still participate in the registration. For better retrieval, we choose the matched point cloud data and use mean squared error results to judge the individual differences of the shape. Finally, the accurate retrieval of the oral shape is realized. Experimental results demonstrate our 3-D shape retrieval algorithm can be successfully searched under different models, which can help forensics use the characteristics of biological individuals to accurately search and identify, and improve recognition efficiency.