Mixing binary face and fingerprint based on Extended Feature Vector (EFV) hashing

Ming Jie Lee; Zhe Jin; Minyi Li; Daniel Bo Wei Chen

doi:10.1109/ISPACS48206.2019.8986315

Mixing binary face and fingerprint based on Extended Feature Vector (EFV) hashing

Ming Jie Lee, Zhe Jin, Minyi Li, Daniel Bo Wei Chen

Malaysia Sch of Information Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

3 Citations (Scopus)

Abstract

Multimodal biometric template protection (BTP) is gaining increasing attention as it overcomes the drawback of unimodal BTP. In this work, a token-less cancellable biometric scheme, namely Extended Feature Vector (EFV) Hashing, is applied to fuse the multimodal binary face and fingerprint template at a feature-level fusion. In advance to the unimodal EFV hashing, this work reorganizes the feature extension mechanism in EFV transformation and introduces the random sampling mechanism to increase the difficulty for reverse processing of the cancellable template. In addition, two fusion options are developed for producing the cancellable template. Experiments are conducted on the well-known Fingerprint FVC2004 and Face LFW datasets. The results demonstrate that the propose approach achieves a satisfactory verification rate with EER 0.1%.

Original language	English
Title of host publication	Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2019)
Editors	Chih-Hsien Hsia, KokSheik Wong
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-2
Number of pages	2
ISBN (Electronic)	9781728130385
ISBN (Print)	9781728130392
DOIs	https://doi.org/10.1109/ISPACS48206.2019.8986315
Publication status	Published - 2019
Event	IEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS) 2019 - Taipei, Taiwan Duration: 3 Dec 2019 → 6 Dec 2019 https://ieeexplore.ieee.org/xpl/conhome/8967510/proceeding (Proceedings)

Conference

Conference	IEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS) 2019
Abbreviated title	ISPACS 2019
Country/Territory	Taiwan
City	Taipei
Period	3/12/19 → 6/12/19
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8967510/proceeding (Proceedings)

Keywords

cancellable biometrics
feature-level fusion
multi-biometrics

Access to Document

10.1109/ISPACS48206.2019.8986315

Cite this

Lee, M. J., Jin, Z., Li, M., & Chen, D. B. W. (2019). Mixing binary face and fingerprint based on Extended Feature Vector (EFV) hashing. In C.-H. Hsia, & K. Wong (Eds.), Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2019) (pp. 1-2). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISPACS48206.2019.8986315

Lee, Ming Jie ; Jin, Zhe ; Li, Minyi et al. / Mixing binary face and fingerprint based on Extended Feature Vector (EFV) hashing. Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2019). editor / Chih-Hsien Hsia ; KokSheik Wong. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 1-2

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abstract = "Multimodal biometric template protection (BTP) is gaining increasing attention as it overcomes the drawback of unimodal BTP. In this work, a token-less cancellable biometric scheme, namely Extended Feature Vector (EFV) Hashing, is applied to fuse the multimodal binary face and fingerprint template at a feature-level fusion. In advance to the unimodal EFV hashing, this work reorganizes the feature extension mechanism in EFV transformation and introduces the random sampling mechanism to increase the difficulty for reverse processing of the cancellable template. In addition, two fusion options are developed for producing the cancellable template. Experiments are conducted on the well-known Fingerprint FVC2004 and Face LFW datasets. The results demonstrate that the propose approach achieves a satisfactory verification rate with EER 0.1%.",

keywords = "cancellable biometrics, feature-level fusion, multi-biometrics",

author = "Lee, {Ming Jie} and Zhe Jin and Minyi Li and Chen, {Daniel Bo Wei}",

note = "Funding Information: This work is partly supported by the Ministry of Science and Technology, Taiwan (107-2218-E-110-013-MY3), NVIDIA Titan Xp GPU grant donation, FRGS (FRGS/1/2018/ICT02/MUSM/03/3). Publisher Copyright: {\textcopyright} 2019 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; IEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS) 2019, ISPACS 2019 ; Conference date: 03-12-2019 Through 06-12-2019",

year = "2019",

doi = "10.1109/ISPACS48206.2019.8986315",

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Lee, MJ, Jin, Z, Li, M & Chen, DBW 2019, Mixing binary face and fingerprint based on Extended Feature Vector (EFV) hashing. in C-H Hsia & K Wong (eds), Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2019). IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 1-2, IEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS) 2019, Taipei, Taiwan, 3/12/19. https://doi.org/10.1109/ISPACS48206.2019.8986315

Mixing binary face and fingerprint based on Extended Feature Vector (EFV) hashing. / Lee, Ming Jie; Jin, Zhe; Li, Minyi et al.
Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2019). ed. / Chih-Hsien Hsia; KokSheik Wong. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 1-2.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

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AU - Lee, Ming Jie

AU - Jin, Zhe

AU - Li, Minyi

AU - Chen, Daniel Bo Wei

N1 - Funding Information: This work is partly supported by the Ministry of Science and Technology, Taiwan (107-2218-E-110-013-MY3), NVIDIA Titan Xp GPU grant donation, FRGS (FRGS/1/2018/ICT02/MUSM/03/3). Publisher Copyright: © 2019 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2019

Y1 - 2019

N2 - Multimodal biometric template protection (BTP) is gaining increasing attention as it overcomes the drawback of unimodal BTP. In this work, a token-less cancellable biometric scheme, namely Extended Feature Vector (EFV) Hashing, is applied to fuse the multimodal binary face and fingerprint template at a feature-level fusion. In advance to the unimodal EFV hashing, this work reorganizes the feature extension mechanism in EFV transformation and introduces the random sampling mechanism to increase the difficulty for reverse processing of the cancellable template. In addition, two fusion options are developed for producing the cancellable template. Experiments are conducted on the well-known Fingerprint FVC2004 and Face LFW datasets. The results demonstrate that the propose approach achieves a satisfactory verification rate with EER 0.1%.

AB - Multimodal biometric template protection (BTP) is gaining increasing attention as it overcomes the drawback of unimodal BTP. In this work, a token-less cancellable biometric scheme, namely Extended Feature Vector (EFV) Hashing, is applied to fuse the multimodal binary face and fingerprint template at a feature-level fusion. In advance to the unimodal EFV hashing, this work reorganizes the feature extension mechanism in EFV transformation and introduces the random sampling mechanism to increase the difficulty for reverse processing of the cancellable template. In addition, two fusion options are developed for producing the cancellable template. Experiments are conducted on the well-known Fingerprint FVC2004 and Face LFW datasets. The results demonstrate that the propose approach achieves a satisfactory verification rate with EER 0.1%.

KW - cancellable biometrics

KW - feature-level fusion

KW - multi-biometrics

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DO - 10.1109/ISPACS48206.2019.8986315

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AN - SCOPUS:85081089320

SN - 9781728130392

SP - 1

EP - 2

BT - Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2019)

A2 - Hsia, Chih-Hsien

A2 - Wong, KokSheik

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - IEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS) 2019

Y2 - 3 December 2019 through 6 December 2019

ER -

Lee MJ, Jin Z, Li M, Chen DBW. Mixing binary face and fingerprint based on Extended Feature Vector (EFV) hashing. In Hsia CH, Wong K, editors, Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2019). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 1-2 doi: 10.1109/ISPACS48206.2019.8986315

Mixing binary face and fingerprint based on Extended Feature Vector (EFV) hashing

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Keywords

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