The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Shape Localization Based on Statistical Method Using Extended Local Binary Pattern
Abstract
Accurate localization of representative points of a face is crucial to many face analysis and synthesis problems. Active shape model (ASM) is a powerful statistical tool for face alignment. However, it suffers from variations of pose, illumination and expressions. In this paper, we analyze the mechanism of active shape model and realize that the ability of normal profiles to describe the local appearance pattern is very limited. For efficient appearance pattern representation, the local binary pattern is used and extended to describe the local patterns of facial key points. For the purpose of retaining the spatial information, sub-images of key points are divided into several regions, which are combined to define the extended local binary pattern (ELBP) histogram. Then we propose an improved ASM method framework, ELBP-ASM, in which local appearance patterns of key points are modelled using extended local binary pattern. Experimental results demonstrate that ELBP-ASM achieves more accurate results compared with original method used in ASM.
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
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882
Cited by
A Completed Modeling of Local Binary Pattern Operator for Texture Classification
Guo, Z., Zhang, L., & Zhang, D. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2044957 · Cited by 2,115
Metadata
- Authors
- Huang, X., Li, S. Z., & Wang, Y.
- Venue
- Third International Conference on Image and Graphics (ICIG'04)
- Publisher
- IEEE
- Published
- 2005-03-31
- Pages
- 184-187
- OpenAlex
- W2096371830
- Cited by
- 178
- References
- 2
- FWCI
- 2.87
- Type
- conference paper
- 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: Shape Localization Based on Statistical Method Using Extended Local Binary Pattern. https://app.cochat.ai/papers/10.1109/icig.2004.127
- The paper
- Huang, X., Li, S. Z., & Wang, Y. (2005). Shape Localization Based on Statistical Method Using Extended Local Binary Pattern. Third International Conference on Image and Graphics (ICIG'04). https://doi.org/10.1109/icig.2004.127
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.
