Direct Regression of Distortion Field from a Single Fingerprint Image
This work addresses a long-standing challenge in biometric security for fingerprint recognition systems, though it appears incremental as it builds on prior rectification methods with improvements in accuracy and pose robustness.
The paper tackles the problem of skin distortion in fingerprint matching, which causes false non-matches, by proposing a method that directly estimates dense distortion fields from a single image, achieving state-of-the-art performance in rectification and matching.
Skin distortion is a long standing challenge in fingerprint matching, which causes false non-matches. Previous studies have shown that the recognition rate can be improved by estimating the distortion field from a distorted fingerprint and then rectifying it into a normal fingerprint. However, existing rectification methods are based on principal component representation of distortion fields, which is not accurate and are very sensitive to finger pose. In this paper, we propose a rectification method where a self-reference based network is utilized to directly estimate the dense distortion field of distorted fingerprint instead of its low dimensional representation. This method can output accurate distortion fields of distorted fingerprints with various finger poses. Considering the limited number and variety of distorted fingerprints in the existing public dataset, we collected more distorted fingerprints with diverse finger poses and distortion patterns as a new database. Experimental results demonstrate that our proposed method achieves the state-of-the-art rectification performance in terms of distortion field estimation and rectified fingerprint matching.