Fingerprint Core Point Detection Algorithm and Implementation

Fingerprint core point detection serves as a critical preprocessing step in biometric identification systems. This technology locates core points (typically the central points with maximum ridge curvature) in fingerprint images by analyzing ridge characteristics, establishing the foundation for subsequent feature matching operations.

The core implementation comprises two key phases:

Directional Field Calculation Phase This stage computes the ridge orientation for each pixel in the fingerprint image using local gradient analysis or frequency-domain methods, forming a directional field matrix. Singular points (such as core points and delta points) manifest as abrupt change regions in the directional field. Preliminary candidate regions can be identified by analyzing directional field consistency or Poincaré index calculations. In code implementation, this typically involves using gradient operators (like Sobel filters) to compute orientation angles, followed by spatial averaging to create a coherent directional map.

Skeletonization Preprocessing Phase The Zhang-Suen classical thinning algorithm is applied to extract skeletons from binarized fingerprint images. This algorithm employs iterative erosion operations to remove boundary pixels while preserving single-pixel-width ridge skeletons. The implementation requires alternating between two sub-iteration patterns that examine different pixel neighborhoods, ensuring precise localization of central intersections in ridge topology during core point detection. The algorithm maintains connectivity while progressively thinning ridges to their central lines.

The technical highlight lies in the synergistic combination of directional field analysis and skeletonization processing: the directional field provides macroscopic ridge flow characteristics, while the skeletonized structure reveals microscopic topological features. Their integration effectively distinguishes genuine core points from pseudo-feature points caused by noise. Practical applications should incorporate confidence scoring mechanisms to eliminate false detections in low-quality image regions, often implemented through reliability thresholds based on directional coherence measures and skeleton connectivity checks.