The DOI matches its Crossref record.
Decision-level fusion in fingerprint verification
Abstract
A scheme is proposed for classifier combination at decision level which stresses the importance of classifier selection during combination. The proposed scheme is optimal (in the Neyman-Pearson sense) when sufficient data are available to obtain reasonable estimates of the join densities of classifier outputs. Four different fingerprint matching algorithms are combined using the proposed scheme to improve the accuracy of a fingerprint verification system. Experiments conducted on a large fingerprint database (∼ 2,700 fingerprints) confirm the effectiveness of the proposed integration scheme. An overall matching performance increase of ∼ 3% is achieved. We further show that a combination of multiple impressions or multiple fingers improves the verification performance by more than 4% and 5%, respectively. Analysis of the results provide some insight into the various decision-level classifier combination strategies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
An introduction to biometric recognition
Jain, A. K., Ross, A., & Prabhakar, S. (2004) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2003.818349 · Cited by 4,974
Self-citationInformation fusion in biometrics
Ross, A., & Jain, A. (2003) · Pattern Recognition Letters · doi:10.1016/s0167-8655(03)00079-5 · Cited by 1,107
Self-citationMultimodal Biometrics: An Overview
Ross, A., & Jain, A. (2004) · Zenodo · doi:10.5281/zenodo.38715 · Cited by 593
Self-citationAcademic Integrity in Online Assessment: A Research Review
Holden, O. L., Norris, M. E., & Kuhlmeier, V. A. (2021) · Frontiers in Education · doi:10.3389/feduc.2021.639814 · Cited by 310
Metadata
- Authors
- Prabhakar, S., & Jain, A. K.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2002-04
- Volume
- 35
- Issue
- 4
- Pages
- 861-874
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2034473161
- Cited by
- 405
- References
- 0
- FWCI
- 31.08
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Decision-level fusion in fingerprint verification. https://app.cochat.ai/papers/10.1016/s0031-3203(01)00103-0
- The paper
- Prabhakar, S., & Jain, A. K. (2002). Decision-level fusion in fingerprint verification. Pattern Recognition. https://doi.org/10.1016/s0031-3203(01)00103-0
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
