As a global feature of fingerprint patterns, the Orientation Field (OF) plays an important role in fingerprint recognition systems. This paper proposes a fast binary pattern based orientation estimation with nearest-neighbor search, which can reduce the computational complexity greatly. We also propose a classified post processing with adaptive averaging strategy to increase the accuracy of the estimated OF. Experimental results confirm that the proposed method can satisfy the strict requirements of the embedded applications over the conventional approaches.

This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. In our previous work, we have already proposed an efficient fingerprint recognition algorithm using Phase-Only Correlation (POC), and developed commercial fingerprint verification units for access control applications. The use of Fourier phase information of fingerprint images makes it possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. This paper presents an idea of improving the performance of POC-based fingerprint matching by combining it with feature-based matching, where feature-based matching is introduced in order to improve recognition efficiency for images with nonlinear distortion. Experimental evaluation using two different types of fingerprint image databases demonstrates efficient recognition performance of the combination of the POC-based algorithm and the feature-based algorithm.

Fingerprint recognition is a well-researched problem, and there are several highly accurate systems commercially available. However, this biometric technology still suffers from problems with the handling of bad quality prints. Recent research has begun to tackle the problems of poor quality data. This paper takes a new approach to one problem besetting fingerprints--that of distortion. Previous attempts have been made to ensure that acquired prints are not distorted, but the novel approach presented here corrects distortions in fingerprints that have already been acquired. This correction is a completely automatic and unsupervised operation. The distortion modelling and correction are explained, and results are presented demonstrating significant improvements in matching accuracy through the application of the technique.