The enhancement performance is assessed on standard fingerprint databases. Experimental results show that the proposed Gabor filtering method can effectively improve the fingerprint image quality and promote the reliability of fingerprint identification.
Step 1: Pattern based algorithms compare the basic fingerprint patterns (arch, whorl, and loop) between a previously stored template and a candidate fingerprint. Step 2: This requires that the images be aligned in the same orientation .
A Fingerprint is scanned and is compared to the template in a database ,if they match valid finger print, If not matched it is Fake.
DATA FLOW DIAGRAM FAKE VALID USER Read image Save image as identifier Smoothing Filtering Scaling Database Verification Remove background images Filtering fake minutae Scaling the length of the image
Analyzed 20 various fingerprint image average result
RING INDEX THUMB Above 95-High accuracy (using algorithm ) 50 % matching is image matched with database( ordinary live scan process) LIVE SCAN MATCHING SCORES 55% 54% 63% ALGORITHM USING MATCHING SCORE 97% 98% 99%
This project demonstrates an efficient fingerprint recognition using minutiae matching, the proposed technique is particularly effective for verifying quality fingerprint images, Every feature was required to match for the entire set to match
Proposed enhancement of algorithm for detection of minutiae give good results in reducing the false minutiae improvements, due to low quality level can also refer to the unification of image filtering and segmentation algorithm and minutiae detection, In order to make the entire process faster