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Face Recognition Using Diamond Sampling Structure Based Local Adaptive Binary Pattern
Subject area: Science,Engineering and Technology · Area of research: Digital Image Processing
Abstract
Local Binary Pattern (LBP) has been widely used in face recognition for its simplicity, but is very sensitive to noise and relies on bilinear interpolation for non-integer neighbor positions. The Binary Rotation Invariant and Noise Tolerant (BRINT) descriptor improved upon LBP through arc-segment averaging, yet still depends on circular sampling requiring interpolation. This paper proposes the Diamond Sampling Structure-Based Local Adaptive Binary Pattern (DLABP) for face recognition. DLABP introduces three contributions: (1) a diamond sampling structure placing all neighbors at integer grid positions, eliminating interpolation entirely; (2) an average method along the radial direction for noise robustness; and (3) a locally adaptive threshold that recovers noise-corrupted nonuniform patterns. DLABP produces a compact 200-dimensional feature and outperforms LBP and BRINT under both noise-free and noisy conditions.
Keywords
Face Recognition, Local Binary Pattern (LBP), BRINT-M, DLABP, Diamond Sampling Structure, Local Adaptive Threshold
References
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How to cite this paper
@article{1715066,
author = {Dr. Nalla Neelima, Siddhardha Shoda, Prathipati Akash Chowdary, Sahukari Dilleswara Rao, Vadranapu Chaitanya Venkata Krishna},
title = {Face Recognition Using Diamond Sampling Structure Based Local Adaptive Binary Pattern},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {910-916},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715066.pdf},
abstract = {Local Binary Pattern (LBP) has been widely used in face recognition for its simplicity, but is very sensitive to noise and relies on bilinear interpolation for non-integer neighbor positions. The Binary Rotation Invariant and Noise Tolerant (BRINT) descriptor improved upon LBP through arc-segment averaging, yet still depends on circular sampling requiring interpolation. This paper proposes the Diamond Sampling Structure-Based Local Adaptive Binary Pattern (DLABP) for face recognition. DLABP introduces three contributions: (1) a diamond sampling structure placing all neighbors at integer grid positions, eliminating interpolation entirely; (2) an average method along the radial direction for noise robustness; and (3) a locally adaptive threshold that recovers noise-corrupted nonuniform patterns. DLABP produces a compact 200-dimensional feature and outperforms LBP and BRINT under both noise-free and noisy conditions.},
keywords = {Face Recognition, Local Binary Pattern (LBP), BRINT-M, DLABP, Diamond Sampling Structure, Local Adaptive Threshold},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715066}
}