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Fruit Classification for Retail Stores Using Deep Learning Read chapter Read first chapter

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

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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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Footnotes
Literature
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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_​4CrossRef
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Cappelli, R., Ferrara, M., Maltoni, D.: Minutia cylinder-code: a new representation and matching technique for fingerprint recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2128–2141 (2010)CrossRef
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Metadata
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
Copyright Year
2020
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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