A B-Spline Function Based 3D Point Cloud Flattening Scheme for 3D Fingerprint Recognition and Identification

3D fingerprint-based recognition and identification have several advantages compared to conventional 2D recognition systems. It is hygienic and secure due to its contactless sample collection. In addition to surface pattern, depth, curvature, and shape information can also be retrieved from 3D fingerprints which can be implemented for designing a more accurate and secure authentication system. While significant progress has been made recently, one of the great challenging issues is the fingerprint pixel’s topological height makes it difficult to extract fingerprint ridge/valley patterns. To address this issue, in this article, the 3D fingerprint represented in 3D point cloud format is flattened using the B-spline curve fitting technique to reduce the impact of the pixel’s topological height. The flattened point cloud is converted to a gray-scale image by using the relative height of the points in the flattened 3D point cloud. The generated gray-scale image is used for recognition via using a conventional 2D fingerprint identification method. The proposed method achieved an Equal Error Rate of 0.2974%, 0.28%, and 0.24% in three experiments, respectively, which is significantly more accurate than the existing methods.