Abstract
Two-dimensional (2D) synthetic fingerprint images have been successfully used for evaluating large-scale automated fingerprint identification systems (AFISs). However, they are limited in assessing the whole process involved in an AFIS, particularly fingerprint image acquisition and fingerprint deformation. Hence, it is desired to develop synthetic 3D fingerprints and 3D fingerprint phantoms. In this reported work, a first attempt to establish a statistical shape model of 3D fingerprints is made by first re-sampling and aligning a set of training 3D fingerprint data and then applying the principal component analysis method. On the basis of the proposed model, synthetic 3D fingerprint shapes can be generated randomly. By further mapping fingerprint textures onto the shapes, synthetic 3D fingerprints can be obtained. Example results are provided demonstrating the effectiveness of the proposed model and method. The model and code will be publicly available for academic purposes.
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