Abstract
An automated fingerprint recognition system (AFRS) for 3D fingerprints is essential and highly promising for biometric security. Despite the progress in developing 3D AFRSs, achieving high-quality real-time reconstruction and high-accuracy recognition of 3D fingerprints remain two challenging issues. To address them, we propose a robust 3D AFRS based on ridge-valley (RV)-guided 3D fingerprint reconstruction and 3D topology polymer (TTP) feature extraction. The former considers the unique fingerprint characteristics of the RV and achieves real-time reconstruction. Unlike traditional triangulation-based methods that establish correspondences between points by cross-correlation-based searching, we propose to establish RV correspondences (RVCs) 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 TTP feature codes the 3D topology by projecting the 3D minutiae onto multiple planes and extracting their corresponding 2D topologies and has proven to be effective and efficient for 3D fingerprint recognition. Comprehensive experimental results demonstrate that our method outperforms the state-of-the-art methods in terms of both reconstruction and recognition accuracy. Also, due to its very short running time, it is appropriate for practical applications.
Highlights
As one of the most reliable and discriminative biometrics, a fingerprint has been widely used in various applications, such as personal electronic products, secure payments, forensics and security [1]
4.1.1 Reconstruction Time Running time is critical for a 3D automated fingerprint recognition system (AFRS) in practical applications
We proposed a robust 3D AFRS based on RV-guided 3D reconstruction and TTP feature extraction
Summary
As one of the most reliable and discriminative biometrics, a fingerprint has been widely used in various applications, such as personal electronic products, secure payments, forensics and security [1]. Its dominance has been established by the continuous emergence of various automated fingerprint recognition systems (AFRSs), with current ones focusing mainly on two-dimensional (2D) contact fingerprints, the acquisition of which requires physical contact between the fingers and sensor’s surface. 2D contact fingerprints are easy to obtain and usually have high ridge-valley (RV) contrasts, the process for capturing them tends to simultaneously introduce inconsistencies and distortions through physical contact, which thereby affects an AFRS’s accuracy [2]. 2D fingerprints cannot truly represent natural three-dimensional (3D) ones because they lose 3D information during acquisition when a curved 3D finger is flattened against a 2D plane. AFRSs for 3D fingerprints have been proposed in recent yeas [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13]
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