Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Robust Algorithm for Contactless Fingerprint Enhancement and Matching

Mahrukh Siddiqui, Shahzaib Iqbal, Bandar AlShammari, Bandar Alhaqbani, Tariq M. Khan, Imran Razzak

TL;DR

This paper addresses the challenges of contactless fingerprint identification by proposing a robust two-phase pipeline that first enhances and encodes minutiae offline, then processes and matches a query fingerprint online against a pre-encoded database. It introduces a Gabor-based enhancement framework guided by locally estimated ridge orientation and frequency, followed by region-quality-based minutiae extraction and a novel n-neighbour minutiae encoding scheme. Matching is performed via an exhaustive, neighbor-aware comparison with complexity $M\times N\times n^{2}$, producing a similarity score. On the PolyU contactless fingerprint dataset, the method achieves a minimum EER of $2.84\%$, outperforming state-of-the-art approaches and delivering competitive matching speed, indicating strong potential for practical contactless fingerprint systems. Future work includes GAN-based enhancement and better integration with forensic workflows.

Abstract

Compared to contact fingerprint images, contactless fingerprint images exhibit four distinct characteristics: (1) they contain less noise; (2) they have fewer discontinuities in ridge patterns; (3) the ridge-valley pattern is less distinct; and (4) they pose an interoperability problem, as they lack the elastic deformation caused by pressing the finger against the capture device. These properties present significant challenges for the enhancement of contactless fingerprint images. In this study, we propose a novel contactless fingerprint identification solution that enhances the accuracy of minutiae detection through improved frequency estimation and a new region-quality-based minutia extraction algorithm. In addition, we introduce an efficient and highly accurate minutiae-based encoding and matching algorithm. We validate the effectiveness of our approach through extensive experimental testing. Our method achieves a minimum Equal Error Rate (EER) of 2.84\% on the PolyU contactless fingerprint dataset, demonstrating its superior performance compared to existing state-of-the-art techniques. The proposed fingerprint identification method exhibits notable precision and resilience, proving to be an effective and feasible solution for contactless fingerprint-based identification systems.

A Robust Algorithm for Contactless Fingerprint Enhancement and Matching

TL;DR

This paper addresses the challenges of contactless fingerprint identification by proposing a robust two-phase pipeline that first enhances and encodes minutiae offline, then processes and matches a query fingerprint online against a pre-encoded database. It introduces a Gabor-based enhancement framework guided by locally estimated ridge orientation and frequency, followed by region-quality-based minutiae extraction and a novel n-neighbour minutiae encoding scheme. Matching is performed via an exhaustive, neighbor-aware comparison with complexity , producing a similarity score. On the PolyU contactless fingerprint dataset, the method achieves a minimum EER of , outperforming state-of-the-art approaches and delivering competitive matching speed, indicating strong potential for practical contactless fingerprint systems. Future work includes GAN-based enhancement and better integration with forensic workflows.

Abstract

Compared to contact fingerprint images, contactless fingerprint images exhibit four distinct characteristics: (1) they contain less noise; (2) they have fewer discontinuities in ridge patterns; (3) the ridge-valley pattern is less distinct; and (4) they pose an interoperability problem, as they lack the elastic deformation caused by pressing the finger against the capture device. These properties present significant challenges for the enhancement of contactless fingerprint images. In this study, we propose a novel contactless fingerprint identification solution that enhances the accuracy of minutiae detection through improved frequency estimation and a new region-quality-based minutia extraction algorithm. In addition, we introduce an efficient and highly accurate minutiae-based encoding and matching algorithm. We validate the effectiveness of our approach through extensive experimental testing. Our method achieves a minimum Equal Error Rate (EER) of 2.84\% on the PolyU contactless fingerprint dataset, demonstrating its superior performance compared to existing state-of-the-art techniques. The proposed fingerprint identification method exhibits notable precision and resilience, proving to be an effective and feasible solution for contactless fingerprint-based identification systems.
Paper Structure (8 sections, 5 equations, 4 figures, 2 tables)

This paper contains 8 sections, 5 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Proposed Methodology
  3. Fingerprint enhancement
  4. Feature Extraction
  5. Feature Encoding
  6. Feature Matching
  7. Results and Discussions
  8. Conclusions

Figures (4)

  • Figure 1: Example images of contactless and contact based fingerprints.
  • Figure 2: Pipeline of the proposed fingerprint recognition system
  • Figure 3: Fingerprint matching based on neighboring Minutia.
  • Figure 4: Visual performance of the proposed method. From left to right input image, the corresponding mask, binary image, thinned image, and minutiae marked on thinned image are illustrated.