A New Technique to Fingerprint Recognition Based on Partial Window

Fingerprint verification is a well-researched problem, and automatic fingerprint verification techniques have been successfully adapted to both civilian and forensic applications for many years. This paper present a new technique to fingerprint recognition based a window that contain  core point this window will be input ANN system to be model we can recognize another fingerprint , so we will firstly,  A recognition algorithm needs to recover fingerprints pose transformation between the input reduce time computation. Our detection algorithm works in the field orientation of the adaptive smoothed with a varying area. The adaptive window is used to attenuate the noise effectively orientation field while maintaining the information of the detailed guidance in the area of ??high curvature. A new approach to the core point location that is proposed is based on hierarchical analysis orientation consistency. The proposed adaptation singular point detection method increases the accuracy of the algorithm. Experiments show that our algorithm developed consistently locates a reference point with high precision only for all fingerprints. And very faster for recognition process. Keywords: Fingerprint recognition; field orientation; neural networks; core point, neural networks

Core Point Pixel-Level Localization by Fingerprint Features in Spatial Domain

Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples