An Efficient Method for Recognizing the Low Quality Fingerprint Verification by Means of Cross Correlation
This addresses fingerprint identification challenges in noisy or damaged images, but appears incremental as it builds on existing correlation techniques.
The paper tackles the problem of low-quality fingerprint verification by proposing a method using cross-correlation and orientation field methodology to improve accuracy in noisy conditions, achieving a good recognition rate.
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.