Face Recognition Using Diamond Sampling Structure Based Local Adaptive Binary Pattern

@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}
  }