detecting fingerprint core using orientation field estimation

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detecting fingerprint core using orientation field estimation

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Detecting the core of a fingerprint using orientation field estimation is a fundamental step in fingerprint recognition and biometric systems. The core point serves as a key reference for alignment, feature extraction, and matching.

The process begins by computing the orientation field, which represents the dominant ridge direction at each pixel in the fingerprint image. Techniques like gradient-based methods or structure tensor analysis are often used to estimate these orientations. Once the orientation field is obtained, the next step is to identify the singular points, including the core—a point where the ridge curvature changes abruptly.

A common approach involves analyzing the Poincaré index, which helps detect loop patterns in the orientation field. The core typically corresponds to a point where the orientation field completes a full rotation (360°). Additional refinements, such as smoothing the orientation field or applying clustering algorithms, can improve accuracy.

This method is widely used in automated fingerprint identification systems (AFIS) due to its robustness against noise and partial fingerprint images. Further optimizations can include machine learning-based verification to reduce false detections.