Method for rapidly positioning robust of finger print core point

Abstract

The invention discloses a fingerprint core point fast robust positioning method, comprising the following steps: images are input and divided; a direction field is calculated; the direction field is smoothed; edge detection is carried out; edge pixel is deleted; for edge pixel points, 4 neighborhoods thereof are applied to calculate gradient information and if the 4 gradient magnitudes calculated are not in the range of a given threshold value (Tl, Th), the pixel is deleted from the edge; the core point is positioned: the outermost pixel of a 3 multiplied by 3 neighborhood window is utilized, the Con, dx and dy values of the rest edge points are calculated respectively and the conditions for positioning the core point are that: when dx is more than Alpha and dy is less than Beta, a point with minimum selected direction consistency is the Lower core point, and when dx is less than Beta and dy is more than Alpha, a point with minimum selected direction consistency is the Upper core point. The method of the invention can not only effectively restrain effect brought by noise, but also can quickly and accurately position the core point of the fingerprint, and reliably detect the core point for all types of fingerprints.

Description

technical field [0001] The invention belongs to a fingerprint recognition method, in particular to a fast and robust positioning method of a fingerprint core point. Background technique [0002] Among many biometric identification systems, fingerprint identification systems are more and more popular due to their advantages of small size, low cost, easy operation, and high reliability. Correspondingly, the market demand for products based on fingerprint identification technology is expanding day by day. is also becoming more widespread. [0003] The core point in the fingerprint image is defined as the point in the direction of maximum curvature. In the fingerprint identification system, core points are often used as reference points to improve the speed and performance of fingerprint matching, and fingerprint classification is mostly realized according to the type, number and relative position of core points in fingerprints. Therefore, accurate and reliable positioning of ...

AI Technical Summary

Problems solved by technology

[0004] Among the different methods for core point detection of fingerprint images, the Poincare index method is a more classic method. Many scholars have made many improvements to this method, but the Poincare index method cannot detect core points for arched fingerprints and is easily affected by noise. and other problems still exist (Asker M.Bazen and Sabih H.Gerez, Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints, IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, 24(7): 905-919.)

Jain et al. proposed the Sine-Map method, which builds a model based on the attributes of the core points for detection through multi-resolution analysis. This method needs to calculate the direction field multiple times, and the computational complexity is very high, and it is not suitable for fingerprint rotation. (Jain, A.K., S. Prabhakar, Hong, L. and Pankanti, S., Filterbank-Based Fingerprint Matching, IEEE Trans. Image Processing, 2000, 9(5): 846-859.)

Others based on mathematical models (Yi Wang, Jiankun Hu, and Damien Phillips, A Fingerprint Orientation Model Based on 2D Fourier Expansion (FOMFE) and Its Application to Singular-Point Detection and Fingerprint Indexing, IEEE Trans. Pattern Analysis and Machine Intelligence, 2007, 29 (4): 573-585.), fingerprint structure (XuchuWang, Jianwei Li and Yanmin Niu, Definition and extraction of stable points from fingerprint images, Pattern Recognition, 2006, 40: 1804-1815.) and multi-scale analysis (ManhuaLiu, Xudong Jiang, and Kot, A.C., Fingerprint Reference-Point Detection, EURASIP Journalon Applied Signal Processing, 2005, 4: 498-509.) method, all require a long processing time, and when the image quality is poor, the core point positioning error Larger, these shortcomings have a greater impact on the performance of the embedded fingerprint recognition system

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