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2020 | OriginalPaper | Chapter

Towards Inpainting and Denoising Latent Fingerprints: A Study on the Impact in Latent Fingerprint Identification

Authors : Ernesto Ramírez-Sáyago, Octavio Loyola-González, Miguel Angel Medina-Pérez

Published in: Pattern Recognition

Publisher: Springer International Publishing

Abstract

In this paper, we provide a study about the impact of the most prominent inpainting and denoising solutions on the latent fingerprint identification. From an in-depth analysis, we show how some of the analyzed inpainting and denoising solutions can improve up 63% for Rank-1 and 26% for Rank-20 the fingerprint identification rates when state-of-the-art minutiae extractors are used. Nevertheless, it is necessary to create new denoising and inpainting solutions that are specifically built to deal with latent fingerprints and their associated issues.

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previous chapter Towards Selecting Reducts for Building Decision Rules for Rule-Based Classifiers

next chapter New Method for Extreme Color Detection in Images

This dataset can be downloaded from http://chalearnlap.cvc.uab.es/dataset/32/description/.

http://github.com/adigasu/FDPMNet.

http://github.com/CVxTz/fingerprint_denoising.

http://github.com/rgsl888/U-Finger-A-Fingerprint-Denosing-Network.

http://biolab.csr.unibo.it.

Adiga V, S., Sivaswamy, J.: FPD-M-net: fingerprint image denoising and inpainting using m-net based convolutional neural networks. In: Escalera, S., Ayache, S., Wan, J., Madadi, M., Güçlü, U., Baró, X. (eds.) Inpainting and Denoising Challenges. TSSCML, pp. 51–61. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-25614-2_4 CrossRef

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Neurotechnology: Verifinger. https://www.neurotechnology.com/verifinger.html

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Title: Towards Inpainting and Denoising Latent Fingerprints: A Study on the Impact in Latent Fingerprint Identification
Authors: Ernesto Ramírez-Sáyago
Octavio Loyola-González
Miguel Angel Medina-Pérez
Publisher: Springer International Publishing
Book: Pattern Recognition
Print ISBN: 978-3-030-49075-1

Electronic ISBN: 978-3-030-49076-8

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-49076-8_8

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