The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Local Directional Number Pattern for Face Analysis: Face and Expression Recognition
Abstract
This paper proposes a novel local feature descriptor, local directional number pattern (LDN), for face analysis, i.e., face and expression recognition. LDN encodes the directional information of the face's textures (i.e., the texture's structure) in a compact way, producing a more discriminative code than current methods. We compute the structure of each micro-pattern with the aid of a compass mask that extracts directional information, and we encode such information using the prominent direction indices (directional numbers) and sign-which allows us to distinguish among similar structural patterns that have different intensity transitions. We divide the face into several regions, and extract the distribution of the LDN features from them. Then, we concatenate these features into a feature vector, and we use it as a face descriptor. We perform several experiments in which our descriptor performs consistently under illumination, noise, expression, and time lapse variations. Moreover, we test our descriptor with different masks to analyze its performance in different face analysis tasks.
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
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
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
Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543262 · Cited by 4,303
Automatic analysis of facial expressions: the state of the art
Pantie, M., & Rothkrantz, L. J. M. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895976 · Cited by 1,778 · cited 1×
“Among these methods are Local Features Analysis [22], Gabor features [23], Elastic Bunch Graph Matching [24], and Local Binary Pattern (LBP) [14], [25].”
Method
Cited by
Lopes, A. T., de Aguiar, E., De Souza, A. F., & Oliveira-Santos, T. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.07.026 · Cited by 799
OFF-ApexNet on micro-expression recognition system
Gan, Y. S., Liong, S.-T., Yau, W.-C., Huang, Y.-C., & Tan, L.-K. (2019) · Signal Processing: Image Communication · doi:10.1016/j.image.2019.02.005 · Cited by 303
Kim, D. H., Baddar, W. J., Jang, J., & Ro, Y. M. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2695999 · Cited by 257
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition
Liong, S.-T., Gan, Y. S., See, J., Khor, H.-Q., & Huang, Y.-C. (2019) · 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019) · doi:10.1109/fg.2019.8756567 · Cited by 253
Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation
Fei, L., Lu, G., Jia, W., Teng, S., & Zhang, D. (2019) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2018.2795609 · Cited by 228
Metadata
- Authors
- Ramirez Rivera, A., Rojas Castillo, J., & Oksam Chae, O.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2013-05
- Volume
- 22
- Issue
- 5
- Pages
- 1740-1752
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W1964762821
- Cited by
- 512
- References
- 5
- FWCI
- 24.92
- 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: Local Directional Number Pattern for Face Analysis: Face and Expression Recognition. https://app.cochat.ai/papers/10.1109/tip.2012.2235848
- The paper
- Ramirez Rivera, A., Rojas Castillo, J., & Oksam Chae, O. (2013). Local Directional Number Pattern for Face Analysis: Face and Expression Recognition. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2012.2235848
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.
