A Translation And Rotation Independent Fingerprint Identification Approach

This thesis describes a new approach for fingerprint identification that will be shift and rotation independent. Detailed descriptions of directional filtering, foreground and background segmentation, feature extraction, and matching based on structural correlation are the main topics of this thesis. The fingerprint identification system consists of image preprocessing, feature extraction, and matching which run on a PC platform. The preprocessing step includes histogram equalization, block-based directional filtering, thinning, and adaptive thresholding to enhance the original images for successful feature extraction. The features extracted will be stored in the database for matching. The matching algorithm presented is a modification and improvement of the structural approach. A two-step process of local feature matching and global feature matching guarantees the correct matching results

OPTIMIZATION OF FINGERPRINT SIZE FOR REGISTRATION

The propose algorithm finds the optimal reduced size of latent fingerprint. The algorithm accelerates the correlation methods of fingerprint registration. The Algorithm is based on decomposition and reduction of fingerprint to one dimension form by using the adoptive method of empirical modes. We choose the most appropriate internal mode to determine the minimum distance between the extremes of empirical modes. We can estimate how many times the fingerprint in the first step of the comparison can be reduced so as not to lose the accuracy of registration. This algorithm shows best results as compared to conventional fingerprint matching techniques that strongly depends on local features for registration. The algorithm was tested on latent fingerprints using FVC2002, FVC2004 and FVC2006 databases

A Correlation-Based Fingerprint Verification System

In this paper, a correlation-based fingerprint verification system is presented. Unlike the traditional minutiae-based systems, this system directly uses the richer gray-scale information of the fingerprints. The correlation-based fingerprint verification system first selects appropriate templates in the primary fingerprint, uses template matching to locate them in the secondary print, and compares the template positions of both fingerprints. Unlike minutiae-based systems, the correlation-based fingerprint verification system is capable of dealing with bad-quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions. Experiments have shown that the performance of this system at the moment is comparable to the performance of many other fingerprint verification systems