The DOI matches its Crossref record.
Joint Biological ID : A Secure and Efficient Lightweight Biometric Authentication Scheme
Abstract
Biometric applications makes biometric authentication replace the traditional password in many cases. Biometric recognition technology has the advantages of convenience and high stability, facilitating identity recognition. However, the shortcoming of biometric authentication is easy to be stolen and leaked, which raises security concerns. In this paper, we design a lightweight joint biometric authentication scheme (SELBA) based on face and fingerprint. We improve searchable encryption (SE) to protect the privacy security of extracted biometric features in the storage and authentication stage. Because of the problem that biometric features cannot be changed or retrieved once leaked in existing schemes, we propose a cancelable mechanism to reconstruct stolen or damaged biometric templates. Moreover, we make a complete security analysis of the SELBA to meet the confidentiality, renewability, revocability, irreversibility and unlinkability of template in biometric recognition. Meanwhile, we conduct experiments on real data sets to show that SELBA is secure, efficient and easy to use in practical application scenarios.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“At the same time, when universities use biometric technologies (such as fingerprint recognition, gait recognition, and voice recognition) for attendance management or campus security monitoring, there is also a potential conflict between data security and user privacy (H.Wang et al., 2022).”
From A Comparative Analysis of AI Privacy Concerns in Higher Education: News Coverage in China and Western Countries (2025) · cites this paper as H.Wang et al., 2022
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 81
- 2021, Statista Res.
- Yang, 2016, Personal information protection legislation of China in Big Data era, J. Nanjing Univ. Posts Telecommun.(Natural Sci. Ed.), 36, 1
- 10.1109/TIFS.2014.2307671 · DOI
- 10.1007/3-540-45539-6_11 · DOI
- 10.1109/ETCHB.2010.5559289 · DOI
- 10.1016/j.neucom.2006.08.009 · DOI
- 10.1109/ICECDS.2017.8389858 · DOI
- 10.1109/TIFS.2007.903543 · DOI
- 10.1109/TPAMI.2009.14 · DOI · Paper page
- 10.1109/TIFS.2012.2223684 · DOI
- 10.1109/TPAMI.2011.34 · DOI
- 10.1109/CCST.2006.313440 · DOI
- 10.1109/ICUMT.2009.5345600 · DOI
- 10.1016/S0020-0190(99)00107-6 · DOI
- Khanban, Computing delaunay triangulation with imprecise input data, 94
- 10.1002/sec.234 · DOI
- 10.1109/TIFS.2008.2011089 · DOI
- 10.1007/978-3-319-49148-6_26 · DOI
- Soon, 2015, Security and privacy protection in visual sensor networks: A survey, Comput. Rev., 56, 255
- 10.1007/978-3-319-20472-7_17 · DOI
- 10.1109/CC.2017.7839758 · DOI
- 10.1109/ACCESS.2018.2889996 · DOI
- 10.1007/springerreference_70850 · DOI
- 10.1109/TIFS.2017.2777340 · DOI
- 10.21437/interspeech.2017-1377 · DOI
- 10.21437/Interspeech.2017-776 · DOI
- 10.1109/TIFS.2014.2349158 · DOI
- 10.1162/jocn.1991.3.1.71 · DOI · Paper page
- 10.1109/CVPR.1991.139758 · DOI
- 10.1109/TNN.2003.813829 · DOI
- 10.1109/CCDC.2013.6561532 · DOI
- 10.1109/TPAMI.2006.244 · DOI
- 10.1109/TPAMI.2018.2800008 · DOI
- Krizhevsky, ImageNet classification with deep convolutional neural networks, 1097
- 10.5244/C.29.41 · DOI · Paper page
- 10.1109/CVPR.2014.220 · DOI · Paper page
- He, 1993, A fast automatic fingerprint identification method based on a weighted-mean of binary image, IEICE Trans. Fundam. Electron., Commun. Comput. Sci., 76, 1469
- 10.1117/1.602066 · DOI
- 10.1117/1.602063 · DOI
- 10.1016/S0143-8166(97)00122-X · DOI
- 10.1117/1.602065 · DOI
- 10.1049/el:19990213 · DOI
- 10.1109/TC.1976.5009253 · DOI
- 10.1109/TPAMI.1980.4767009 · DOI
- 10.1109/TNNLS.2021.3066381 · DOI
- 10.1109/JIOT.2021.3074952 · DOI
- 10.1109/JBHI.2021.3109595 · DOI
- Ross, 2006, Handbook of Multibiometrics (International Series on Biometrics)
- 10.1016/j.inffus.2015.06.007 · DOI
- Jain, 1999, A multimodal biometric system using fingerprint, face and speech, 182
- 10.1109/TPAMI.2003.1227990 · DOI · Paper page
- 10.1006/dspr.2001.0397 · DOI
- 10.1016/j.sigpro.2006.02.045 · DOI
- 10.1109/TIFS.2020.3033189 · DOI
- 10.1109/TMM.2021.3071243 · DOI
- 10.1145/2508859.2516700 · DOI
- 10.1007/978-3-540-74835-9_24 · DOI
- Song, Timing analysis of keystrokes and timing attacks on SSH
- 10.1145/2307636.2307666 · DOI
- 10.1145/2976749.2978397 · DOI
- 10.1108/sr.2003.08723dad.008 · DOI
- 10.1016/S0969-4765(18)30010-9 · DOI
- Kings, 2014, Samsung galaxy note 4: An easy guide to the note 4s best features, Samsung
- 10.1109/BTAS.2013.6712736 · DOI
- 10.1109/TCE.2015.7150600 · DOI
- 10.1016/j.patrec.2011.09.041 · DOI
- 10.1145/2449396.2449432 · DOI
- 10.1109/IJCB.2011.6117510 · DOI
- 10.1109/TIFS.2014.2322255 · DOI
- Aviv, Smudge attacks on smartphone touch screens, 1
- 10.1109/TPAMI.1986.4767798 · DOI
- 10.1109/CVPR.2007.383385 · DOI
- 10.1016/0031-3203(95)00067-4 · DOI · Paper page
- 10.1109/TPAMI.2007.1004 · DOI
- 10.1016/j.patcog.2005.10.025 · DOI
- 10.1109/ICPR.2008.4761886 · DOI
- 10.1016/j.patcog.2007.12.002 · DOI · Paper page
- 10.1109/TIFS.2018.2838540 · DOI
- 10.1109/TCC.2018.2866405 · DOI
- 10.1007/978-3-540-74549-5_42 · DOI
- 10.1109/ICBAKE.2013.90 · DOI
Cited by
Sorted by citations
Xue, Y., Chinapah, V., & Zhu, C. (2025) · Education Sciences · doi:10.3390/educsci15060650 · Cited by 30
Identifying Who You Are No Matter What You Write Through Abstracting Handwriting Style
Huang, J.-Y., Feng, Y.-H., Cui, F.-Q., Zhang, X., Liu, Z., Liu, X., Liu, J.-C., Zhang, F.-S., & Li, M. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3668275 · Cited by 20
“Furthermore, dataset limitations are rooted in constraints related to data collection conditions [3], [4], [5], [6].”
Designing Anonymous Signature-Based Identity Authentication Scheme for Ocean Multilevel Transmission
Ye, J., Cao, X.-H., & Xie, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3390176 · Cited by 8
“Comparing with traditional identity authentication methods, Wang et al. [6] proposed a method using biological characteristics for identity authentication, allowing a rapid identity recognition process and effectively improving the overall efficiency of the system.”
MethodBIAS: A novel secure and efficient biometric-based anonymous authentication scheme
Zhu, Q., You, L., Hu, G.-R., & Liu, W.-N. (2024) · Comput. Networks · doi:10.1016/j.comnet.2024.110754 · Cited by 3
Xu, R., Feng, M., Yang, J., Xie, J.-Y., Tian, Y., Sang, X., Wang, J.-R., Li, Y.-Y., Khan, A., & Song, F. (2024) · Zhongguó wùli xuékan · doi:10.1016/j.cjph.2024.08.025 · Cited by 2
HKNet: Rethinking palm vein recognition framework with hybrid key set score
Xie, T.-M., & Kang, W.-X. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113307 · Cited by 1
Robust embedded access control system based on face and encrypted QR with RPi4
Hammami, S., & Alhammami, M. (2024) · International Journal of Reconfigurable and Embedded Systems (IJRES) · doi:10.11591/ijres.v13.i3.pp586-594 · Cited by 1
Troshkov, A., Ermakova, A. N., Bogdanova, S., Gajchuk, D. V., & Ponomarenko, M. (2026) · International Conference on Digital Transformation: Informatics, Economics, and Education · doi:10.1117/12.3120745
“In parallel, lightweight secure biometric authentication schemes aimed at practical deployment in resource-constrained digital environments are being developed 2 .”
Gong, W., & Liu, S.-L. (2026) · Theoretical Computer Science · doi:10.1016/j.tcs.2026.116079
Metadata
- Authors
- Wang, H., Fan, K., Chen, H., Chen, Q., Zhang, K., Li, F., Li, H., & Yang, Y.
- Affiliations
- Xidian University (CN)
- University of Nebraska–Lincoln (US)
- Venue
- IEEE Transactions on Dependable and Secure Computing
- Published
- 2022
- Pages
- 1-16
- ISSN
- 1545-5971 (ISSN-L)
- 1941-0018
- 2160-9209
- OpenAlex
- W4285229412
- Cited by
- 6
- References
- 0
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 1
- FWCI
- 0.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Dependable and Secure Computing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Authentication Protocols Security
- Keywords
- biometric authentication, searchable encryption, biometric template protection, template leakage, lightweight authentication scheme, biometric privacy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
