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Fingermark quality assessment framework with classic and deep learning ensemble models

  • Published:2022-08 Issue: Volume:250 Page:109148
  • ISSN:0950-7051
  • Container-title:Knowledge-Based Systems
  • language:en
  • Short-container-title:Knowledge-Based Systems

Author:

Oblak TimORCID,Haraksim Rudolf,Peer PeterORCID,Beslay Laurent

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference94 articles.

1. Anatomy of friction ridge skin;Hicklin,2009

2. Expert Witnesses: in Defence of Expert Witnesses in General and of Document Examiners in Particular;Huber,1959

3. Quantitative-Qualitative Friction Ridge Analysis : an Introduction to Basic and Advanced Ridgeology;Ashbaugh,1999

4. Latent fingerprint quality: A survey of examiners;Hicklin;J. Forensic Identif.,2011

5. Accuracy and reliability of forensic latent fingerprint decisions;Ulery;Proc. Natl. Acad. Sci.,2011

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1. Recovery of Incomplete Fingerprints Based on Ridge Texture and Orientation Field;Electronics;2024-07-21

2. Tiny Partial Fingerprint Sensor Quality Assessment;IEEE Sensors Letters;2024-05

3. Multi-Surface Multi-Technique (MUST) Latent Fingerprint Database;IEEE Transactions on Information Forensics and Security;2024

4. A Latent Fingerprint in the Wild Database;IEEE Transactions on Information Forensics and Security;2024

5. Advancing Fingerprint Recognition Quality Assessment: Introducing the FRBQ Metric for Enhanced Fingerprint Recognition;Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing;2023-12-15
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