3D Fingerprint Recognition based on Ridge-valley-guided 3D Reconstruction and 3D Topology Polymer Feature Extraction.

Automated fingerprint recognition system (AFRS) for 3D fingerprints is essential and highly promising in biometric security. Despite the progress in 3D AFRSs, high-quality real-time 3D fingerprint reconstruction and high-accuracy 3D fingerprint recognition remain two challenging issues. To address these issues, we propose a robust 3D AFRS based on ridge-valley-guided 3D fingerprint reconstruction and 3D topology feature extraction. The proposed 3D fingerprint reconstruction considers the unique fingerprint characteristic of ridge-valley (RV) and achieves real-time reconstruction. Different from traditional triangulation-based methods that establish correspondence between points by cross-correlation-based searching, we propose to establish RV correspondence (RVC) between ridges/valleys by defining and calculating a RVC matrix based on the topology of RV curves. To enhance depth reconstruction, curve-based smoothing is proposed to refine our novel RV disparity map. The proposed 3D fingerprint recognition is based on three-dimensional topology polymer (TTP) feature extraction. The TTP codes 3D topology by projecting the 3D minutiae onto multiple planes and extracting their corresponding 2D topologies, which has proven to be effective and efficient. Comprehensive experimental results demonstrate that our method outperforms the state-of-the-art methods in terms of both reconstruction and recognition accuracy. Thanks to the significantly short running time, our method is applicable to practical applications.