Study of Fingerprint Enhancement and Matching

Fingerprint is the oldest and popular form of bio-metric identification. Extract Minutiae is most used method for automatic fingerprint matching, every person fingerprint has some unique characteristics called minutiae. But studying the extract minutiae from the fingerprint images and matching it with database is depend on the image quality of finger impression. To make sure the performance of finger impression identification we have to robust the quality of fingerprint image by a suitable fingerprint enhancement algorithm. Here we work with a quick finger impression enhancement algorithm that improve the lucidity of valley and ridge structure based on estimated local orientation and frequency. After enhancement of sample fingerprint, sample fingerprint is matched with the database fingerprints, for that we had done feature extraction, minutiae representation and registration. But due to Spurious and missing minutiae the accuracy of fingerprint matching affected. We had done a detail relevant finger impression matching method build on the Shape Context descriptor, where the hybrid shape and orientation descriptor solve the problem. Hybrid shape descriptor filter out the unnatural minutia paring and ridge orientation descriptor improve the matching score. Matching score is generated and utilized for measuring the accuracy of execution of the proposed algorithm. Results demonstrated that the algorithm is exceptionally satisfactory for recognizing fingerprints acquired from diverse sources. Experimental results demonstrate enhancement algorithm also improves the matching accuracy

Fingerprint Verification using Steerable Filters”,

ABSTRACT In this paper, fingerprint verification using steerable filters is presented. The existing fingerprint recognition systems are based on minutiae matching. The common fingerprint matching schemes are Correlation-based matching, Minutiae-based matching and Ridge feature -based matching. The minutiae-based matching systems are the most widely used and popular. The minutiae extraction undergoes very critical steps (like binerization, thinning) and which affects on the overall accuracy of the system. Poor ridge structure and the image processing articrafts may introduce spurious minutiae. A frequency selective as well as orientation selective transform like Gabor transform has been used for extracting the texture features. This paper describes a novel approach based on steerable wedge filter. The proposed method is capable of finding the texture features of fingerprint image irrespective to the image quality in terms of average gray level, clarity in the ridges and comparatively with fewer computations

Orientation Field Estimation for Latent Fingerprint Using Region Segmentation

ABSTRACT:Latent fingerprint matching has played a critical role in identifying suspects and criminals. However, compared torolled and plain fingerprint matching, latent fingerprint identification accuracy is much lower due to complex background noise,poor ridge quality and overlapping structured noise in latent images. Accordingly, manual markup of various features (e.g.,region of interest, singular points and minutiae) is typically necessary to extract reliable features from latents. To reduce thismarkup cost and to improve the consistency in feature markup, fully automatic and highly accurate latentmatching algorithms are needed. In this paper, we propose an automatic region segmentation algorithm whose goal is to separate the fingerprint region (region of interest) from background. It utilizes both ridge orientation and frequency features. The orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation, and local Fourier analysis method is used to estimate the local ridge frequency of the latent fingerprint. Candidate fingerprint (foreground) regions are obtained for each feature type; an intersection of regions from orientation and frequency features localizes the true latent fingerprint regions. To verify the viability of the proposed region segmentation algorithm, we evaluated the segmentation results in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region

A DoG based Approach for Fingerprint Image Enhancement

Fingerprints have been the most accepted tool for personal identification since many decades. It is also an invaluable tool for law enforcement and forensics for over a century, motivating the research in Automated fingerprint-based identification, an application of biometric system. The matching or identification accuracy using fingerprints has been shown to be very high. The theory on the uniqueness of fingerprint minutiae leads to the steps in studying the statistics of extracting the minutiae features reliably. Fingerprint images obtained through various sources are rarely of perfect quality. They may be degraded or noisy due to variations in skin or poor scanning technique or due to poor impression condition. Hence enhancement techniques are applied on fingerprint images prior to the minutiae point extraction to get sure of less spurious and more accurate minutiae points from the reliable minutiae location. This thesis focuses on fingerprint image enhancement techniques through histogram equalization applied locally on the degraded image. The proposed work is based on the Laplacian pyramid framework that decomposes the input image into a number of band-pass images to improve the local contrast, as well as the local edge information. The resultant image is passed through the regular methodologies of fingerprint, like ridge orientation, ridge frequency calculation, filtering, binarization and finally the morphological operation thinning. Experiments using different texture of images are conducted to enhance the images and to show a comparative result in terms of number of minutiae extracted from them along with the spurious and actual number existing in each enhanced image. Experimental results out performs well to overcome the counterpart of enhancement technique