An efficient and effective pore matching method using ResCNN descriptor and local outliers

With the advancement of high-resolution fingerprint scanners, sweat pores have emerged as a robust biometric feature for fingerprint representation and recognition. Numerous pore-matching algorithms have been developed to enhance the accuracy of automatic fingerprint recognition systems (AFRSs). However, existing models often suffer from inefficiencies and poor generalization performance. This article introduces a novel method that balances efficiency and effectiveness. After fingerprints are aligned and pores are annotated, a ResCNN-based pore descriptor is designed to capture both static and dynamic features of sweat pores, with an emphasis on inter-class differences and intra-class similarities. This leads to the generation of robust descriptors that can handle variations such as deformation and pressure changes. Additionally, the AdaLAM algorithm is refined to efficiently remove local outliers, which improves matching accuracy and reduces computational time. To adapt to different scenarios, different strategies are employed for partial and full fingerprint recognition. For partial fingerprints, the method addresses the challenge of small overlapping areas by incorporating distinctive pore matching results using AdaLAM. For full fingerprints, the method trains image descriptors and integrates fingerprint similarity with pore matching to further enhance accuracy. Experiments on the benchmark PolyU-HRF dataset demonstrate that the algorithm achieves an equal error rate (EER) of 1.71% for DBI (partial fingerprints) and 0.02% for DBII (full fingerprints). Compared to current state-of-the-art approaches, the method reduces the False Match Rate 1000 (FMR1000) by 38.88% for partial fingerprints and 100% for full fingerprints, with a speed improvement of approximately 90 times.