Face Recognition Using Diamond Sampling Structure Based Local Adaptive Binary Pattern

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.