The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Finger Vein Recognition Using Local Line Binary Pattern
Open accessAbstract
In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbourhood shape in LLBP is a straight line, unlike in local binary pattern (LBP) which is a square shape. Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP).
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
Cites
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
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
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions
Tan, X., & Triggs, B. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2042645 · Cited by 2,842
Zhang, B., Gao, Y., Zhao, S., & Liu, J. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2035882 · Cited by 1,024
Miura, N., Nagasaka, A., & Miyatake, T. (2004) · Machine Vision and Applications · doi:10.1007/s00138-004-0149-2 · Cited by 772
Local binary patterns variants as texture descriptors for medical image analysis
Nanni, L., Lumini, A., & Brahnam, S. (2010) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2010.02.006 · Cited by 507
Robust Facial Expression Recognition Based on Local Directional Pattern
Jabid, T. (2010) · ETRI Journal · doi:10.4218/etrij.10.1510.0132 · Cited by 356
Palmprint verification using binary orientation co-occurrence vector
Guo, Z., Zhang, D. D., Zhang, L., et al. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.05.010 · Cited by 289
New Finger Biometric Method Using Near Infrared Imaging
Lee, E. C., Jung, H., & Kim, D. (2011) · Sensors · doi:10.3390/s110302319 · Cited by 241
Lee, E. C., Lee, H. C., & Park, K. R. (2009) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.20193 · Cited by 215
Cited by
Mohd Asaari, M. S., Suandi, S. A., & Rosdi, B. A. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.11.033 · Cited by 342
Self-citationConvolutional Neural Network for Finger-Vein-Based Biometric Identification
Das, R., Piciucco, E., Maiorana, E., & Campisi, P. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2850320 · Cited by 313
Metadata
- Authors
- Rosdi, B. A., Shing, C. W., & Suandi, S. A.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2011-11-30
- Volume
- 11
- Issue
- 12
- Pages
- 11357-11371
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2039536345
- Cited by
- 279
- References
- 15
- FWCI
- 17.67
- Type
- journal article
- Language
- en
- Open access
- gold
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: Finger Vein Recognition Using Local Line Binary Pattern. https://app.cochat.ai/papers/10.3390/s111211357
- The paper
- Rosdi, B. A., Shing, C. W., & Suandi, S. A. (2011). Finger Vein Recognition Using Local Line Binary Pattern. Sensors. https://doi.org/10.3390/s111211357
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.
