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

Analysis and detection of fingerprint creases

Fingerprint is a biometric trait that is widely used for human identification and verification. Most fingerprint biometric systems make use of certain salient features on the fingerprint, including minutiae points, pores, and singular points, for comparing two fingerprint images. In this work, we explore the possibility of using fingerprint creases for comparing two fingerprints. Creases can be described as white lines or scars on a fingerprint image. Recent studies have determined that some creases are genetically influenced although the origin of creases has not been completely characterized. While no published work exists for crease matching, some studies have explored the problem of automated crease detection. In this thesis, we study the possibility of using creases for fingerprint matching. We also suggest two techniques to automatically extract creases from an input fingerprint image. Finally, we study the correlation between fingerprint creases and age of an individual

Enhancement of Latent Fingerprint Recognition Using Global Transform

Latent Fingerprints plays a vital role in identifying thefts, crime etc. Latent fingerprints are of 3 types. Noise in the Latent Fingerprints is removed by smoothing. Manual marking in Latent Fingerprint is slow and also latent examiner may make mistake while marking. The minutiae in the same latent marked by different latent examiners or even by the same examiner (but at different times) may not be the same. To overcome this issue new Orientation field estimation algorithm is introduced. It based on latent fingerprint feature extraction and edge detection. Orientation field estimation algorithm has dictionary construction stage. Dictionary Construction has 2 Stages. i) Offline stage ii) online stage. Orientation field estimation algorithm is applied for Overlapped fingerprint. Hough transform is used for detecting edges. It is shown that this method is slower to recognize latent fingerprint feature extraction and edge linking. In order to further increase the speed and perfect edge linking Hough transform method can be modified for better performance. Global transform is used for perfect edge linking and get the full fingerprint structure and comparison is made between two transforms to show which transform is better. DOI: 10.17762/ijritcc2321-8169.15034

Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review

This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen

Advanced Partial Palmprint Matching Based on Repeated Adjoining Minutiae

Nowadays, high resolution palmprint images are used for recognition. The features that can be extracted from a high resolution palmprint image include the minutiae points. In this paper, instead of full palmprints, partial palmprints are used for matching. Partial refers to a part of the palmprint such as the thenar and hypothenar or hypothenar and interdigital areas. The minutiae can be easily located from the thinned palmprint image by using a window. Since there are a large number of minutiae present within a palmprint image, the minutiae are grouped into several clusters. The extracted minutiae are clustered using Hough circles. In order to avoid spurious minutiae resulting from the presence of immutable creases, radon transform is made use of. By selecting initial minutiae pairs, the entire matching is done by using repeated adjoining minutiae matching. The algorithm is developed and successfully tested with palmprint database. DOI: 10.17762/ijritcc2321-8169.15017