Context-Aware Restoration of Noisy Fingerprints

Joshi, Indu; Prakash, Tushar; Jaiswal, B. S.; Kumar, Rohit; Dantcheva, Antitza; Roy, Sumantra Dutta; Kalra, Prem Kumar

doi:10.1109/lsens.2022.3203787

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Cited by 3 publications

References 29 publications

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Unsupervised domain alignment of fingerprint denoising models using pseudo annotations

Joshi,

Prakash,

Kumar

et al. 2023

Multimed Tools Appl

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Unsupervised domain alignment of fingerprint denoising models using pseudo annotations

Joshi,

Prakash,

Kumar

et al. 2023

Multimed Tools Appl

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Leveraging Explainable Artificial Intelligence for Understanding the Effect of Model Capacity on Training Dataset Size

Prakash,

Dhamija,

Kumar

et al. 2022

2022 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

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Semi-automatic fingerprint image restoration algorithm using a partial differential equation

Lee,

Kim,

Kwak

et al. 2023

MATH

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<abstract><p>A fingerprint is the unique, complex pattern of ridges and valleys on the surface of an individual's fingertip. Fingerprinting is one of the most popular and widely used biometric authentication methods for personal identification because of its reliability, acceptability, high level of security, and low cost. When using fingerprints as a biometric, restoring poor-quality or damaged fingerprints is an essential process for accurate verification. In this study, we present a semi-automatic fingerprint image restoration method using a partial differential equation to repair damaged fingerprint images. The proposed algorithm is based on the Cahn-Hilliard (CH) equation with a source term, which was developed for simulating pattern formation during the phase separation of diblock copolymers in chemical engineering applications. In previous work, in order to find an optimal model and numerical parameter values in the governing equation, we had to make several trial and error preliminary attempts. To overcome these problems, the proposed novel algorithm minimizes user input and automatically computes the necessary model and numerical parameter values of the governing equation. Computational simulations on various damaged fingerprint samples are presented to demonstrate the superior performance of the proposed method.</p></abstract>

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Context-Aware Restoration of Noisy Fingerprints

Cited by 3 publications

References 29 publications

Unsupervised domain alignment of fingerprint denoising models using pseudo annotations

Unsupervised domain alignment of fingerprint denoising models using pseudo annotations

Leveraging Explainable Artificial Intelligence for Understanding the Effect of Model Capacity on Training Dataset Size

Semi-automatic fingerprint image restoration algorithm using a partial differential equation

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