ArticlePattern recognition2025
Local Sliced Wasserstein Feature Sets for Illumination Invariant Face Recognition.
Article in Pattern recognition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed.
- Linear Optimal Transport Subspaces for Point Set Classification.Journal of mathematical imaging and vision · 2025Article
- Quantifying Nuclear Structures of Digital Pathology Images Across Cancers Using Transport-Based Morphometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2025Article
- Augmenting early stroke diagnosis with an eye-tracker.Frontiers in neurology · 2025Article
- Linear optimal transport subspaces for point set classification.Research square · 2024Article
- Invariance encoding in sliced-Wasserstein space for image classification with limited training data.Pattern recognition · 2023Article
- Predicting Malignancy of Breast Imaging Findings Using Quantitative Analysis of Contrast-Enhanced Mammography (CEM).Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
We present a new method for face recognition from digital images acquired under varying illumination conditions. The method is based on mathematical modeling of local gradient distributions using the Radon Cumulative Distribution Transform (R-CDT) [1]. We demonstrate that lighting variations cause certain types of deformations of local image gradient distributions which, when expressed in R-CDT domain, can be modeled as a subspace. Face recognition is then performed using a nearest subspace method in R-CDT domain of local gradient distributions. Experimental results demonstrate the proposed method outperforms other alternatives in several face recognition tasks with challenging illumination conditions. Python code implementing the proposed method is publicly available, which is integrated as a part of the software package PyTransKit.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.