most citedRegression of Dense Distortion Field from a Single Fingerprint Image

7 citations · 10 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Minutiae-Anchored Local Dense Representation for Fingerprint Matching

Zhiyu Pan, Xiongjun Guan, Yongjie Duan +2

Fingerprint matching under diverse capture conditions remains a fundamental challenge in biometric recognition. To achieve robust and accurate performance in such scenarios, we pro…

cs.CV2025

Fixed-Length Dense Fingerprint Representation with Alignment and Robust Enhancement

Zhiyu Pan, Xiongjun Guan, Yongjie Duan +2

Fixed-length fingerprint representations, which map each fingerprint to a compact and fixed-size feature vector, are computationally efficient and well-suited for large-scale match…

cs.CV2024

Latent Fingerprint Matching via Dense Minutia Descriptor

Zhiyu Pan, Yongjie Duan, Xiongjun Guan +2

Latent fingerprint matching is a daunting task, primarily due to the poor quality of latent fingerprints. In this study, we propose a deep-learning based dense minutia descriptor (…

cs.CV20247 cited

Regression of Dense Distortion Field from a Single Fingerprint Image

Xiongjun Guan, Yongjie Duan, Jianjiang Feng +1

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 estim…

cs.CV20243 cited

Direct Regression of Distortion Field from a Single Fingerprint Image

Xiongjun Guan, Yongjie Duan, Jianjiang Feng +1

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 estim…