Fingerprint Recognition Entropy Map Feature Point Ranking

The patent segments the fingerprint image into multiple feature point levels (first-level, second-level, third-level feature points) based on their importance and characteristics. This segmentation allows the system to process and compare critical feature points more thoroughly, improving recognition accuracy without requiring complete processing of all image data, thus managing complexity effectively.

The patent applies local quality by treating different feature points with different levels of analysis and comparison depth. High-importance feature points receive more rigorous comparison procedures, while less critical points use simplified methods. This differential approach optimizes accuracy for the most discriminative features while controlling overall computational complexity.

The patent segments image information processing by prioritizing feature points in hierarchical levels. First-level feature points (most important) are processed and compared first, providing rapid initial matching. Second and third-level points are processed subsequently with decreasing intensity. This segmentation enables the system to achieve high accuracy through progressive refinement without requiring complete processing of all image data simultaneously, thus reducing overall processing time.

The patent performs preliminary classification and ranking of feature points before the actual comparison process. By pre-identifying and prioritizing the most discriminative feature points, the system prepares the data in advance to enable faster comparison operations. This preliminary action ensures that the most informative features are processed first, achieving accurate recognition with reduced processing time.

The patent segments feature points into distinct hierarchical levels (first-level, second-level, third-level) based on their characteristics and importance. This segmentation creates a structured framework that simplifies the comparison process by treating each level with appropriate processing intensity. The clear segmentation reduces the complexity of managing all feature points uniformly, as each level has defined processing rules.

The patent applies local quality by assigning different processing qualities and comparison depths to different feature point levels. First-level feature points receive the most rigorous analysis, while lower-level points use simplified comparison methods. This differentiated approach optimizes accuracy for critical features while reducing computational complexity for less important points, achieving a balance between precision and method complexity.