Denoising and Inpainting On-Display Fingerprints Using a Novel Dilated Auto-encoder Network

Shi Xian Zhuang; Chao Yuan Zheng; Pei Yung Hsiao

doi:10.1109/ICCE-Taiwan55306.2022.9869021

Denoising and Inpainting On-Display Fingerprints Using a Novel Dilated Auto-encoder Network

Shi Xian Zhuang
, Chao Yuan Zheng
, Pei Yung Hsiao

National University of Kaohsiung

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

For on-display fingerprint (ODF) sensors, inpainting and noise removal is an important step to achieve the subsequent fingerprint recognition algorithm. This paper presents four advanced CNNs to enhance and clarify the quality of ODF images. Moreover, the pragmatic ODF databases and thinned ground truths, GTs, are used for testing and similarity verification. Experiments show that our novel dilated autoencoder CNN makes the best of the ODF image quality performance and the training efficiency.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	117-118
Number of pages	2
ISBN (Electronic)	9781665470506
DOIs	https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869021
State	Published - 2022
Externally published	Yes
Event	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 - Taipei, Taiwan Duration: 06 07 2022 → 08 07 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Conference

Conference	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Country/Territory	Taiwan
City	Taipei
Period	06/07/22 → 08/07/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

CNN
ODF sensor
U-net
autoencoder
deep learning
fingerprint denoising and inpainting
smartphone

Access to Document

10.1109/ICCE-Taiwan55306.2022.9869021

Cite this

Zhuang, S. X., Zheng, C. Y., & Hsiao, P. Y. (2022). Denoising and Inpainting On-Display Fingerprints Using a Novel Dilated Auto-encoder Network. In Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 (pp. 117-118). (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869021

Zhuang, Shi Xian ; Zheng, Chao Yuan ; Hsiao, Pei Yung. / Denoising and Inpainting On-Display Fingerprints Using a Novel Dilated Auto-encoder Network. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 117-118 (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022).

@inproceedings{dd886de198984f5d847d3e8a139ce623,

title = "Denoising and Inpainting On-Display Fingerprints Using a Novel Dilated Auto-encoder Network",

abstract = "For on-display fingerprint (ODF) sensors, inpainting and noise removal is an important step to achieve the subsequent fingerprint recognition algorithm. This paper presents four advanced CNNs to enhance and clarify the quality of ODF images. Moreover, the pragmatic ODF databases and thinned ground truths, GTs, are used for testing and similarity verification. Experiments show that our novel dilated autoencoder CNN makes the best of the ODF image quality performance and the training efficiency.",

keywords = "CNN, ODF sensor, U-net, autoencoder, deep learning, fingerprint denoising and inpainting, smartphone",

author = "Zhuang, \{Shi Xian\} and Zheng, \{Chao Yuan\} and Hsiao, \{Pei Yung\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 ; Conference date: 06-07-2022 Through 08-07-2022",

year = "2022",

doi = "10.1109/ICCE-Taiwan55306.2022.9869021",

language = "英语",

series = "Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "117--118",

booktitle = "Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022",

address = "美国",

}

Zhuang, SX, Zheng, CY & Hsiao, PY 2022, Denoising and Inpainting On-Display Fingerprints Using a Novel Dilated Auto-encoder Network. in Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Institute of Electrical and Electronics Engineers Inc., pp. 117-118, 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Taipei, Taiwan, 06/07/22. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869021

Denoising and Inpainting On-Display Fingerprints Using a Novel Dilated Auto-encoder Network. / Zhuang, Shi Xian; Zheng, Chao Yuan; Hsiao, Pei Yung.
Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 117-118 (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Denoising and Inpainting On-Display Fingerprints Using a Novel Dilated Auto-encoder Network

AU - Zhuang, Shi Xian

AU - Zheng, Chao Yuan

AU - Hsiao, Pei Yung

PY - 2022

Y1 - 2022

N2 - For on-display fingerprint (ODF) sensors, inpainting and noise removal is an important step to achieve the subsequent fingerprint recognition algorithm. This paper presents four advanced CNNs to enhance and clarify the quality of ODF images. Moreover, the pragmatic ODF databases and thinned ground truths, GTs, are used for testing and similarity verification. Experiments show that our novel dilated autoencoder CNN makes the best of the ODF image quality performance and the training efficiency.

AB - For on-display fingerprint (ODF) sensors, inpainting and noise removal is an important step to achieve the subsequent fingerprint recognition algorithm. This paper presents four advanced CNNs to enhance and clarify the quality of ODF images. Moreover, the pragmatic ODF databases and thinned ground truths, GTs, are used for testing and similarity verification. Experiments show that our novel dilated autoencoder CNN makes the best of the ODF image quality performance and the training efficiency.

KW - CNN

KW - ODF sensor

KW - U-net

KW - autoencoder

KW - deep learning

KW - fingerprint denoising and inpainting

KW - smartphone

UR - https://www.scopus.com/pages/publications/85138716805

U2 - 10.1109/ICCE-Taiwan55306.2022.9869021

DO - 10.1109/ICCE-Taiwan55306.2022.9869021

M3 - 会议稿件

AN - SCOPUS:85138716805

T3 - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

SP - 117

EP - 118

BT - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Y2 - 6 July 2022 through 8 July 2022

ER -

Zhuang SX, Zheng CY, Hsiao PY. Denoising and Inpainting On-Display Fingerprints Using a Novel Dilated Auto-encoder Network. In Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 117-118. (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022). doi: 10.1109/ICCE-Taiwan55306.2022.9869021

Denoising and Inpainting On-Display Fingerprints Using a Novel Dilated Auto-encoder Network

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this