An automatic fingerprint classification technique based on global features

Fingerprint classification is an important stage in automatic fingerprint identification system (AFIS) because it significantly reduces the processing time to search and retrieve in a large-scale fingerprint database. However, its performance is heavily relied on image quality that comes in various forms such as low contrast, wet, dry, bruise, cuts, stains, etc. This paper proposed an automatic fingerprint classification scheme based on singular points and structural shape of orientation fields. It involves several steps, amongst others: firstly, fingerprint foreground is extracted and then noise patches in the foreground are detected and enhanced. Next, the orientation fields are estimated, and a corrective procedure is performed on the false ones. Afterward, an orientation image is created and singular points are detected. Based on the number of core and delta and their locations, an exclusive membership of the fingerprint can be discovered. Should it fail, the structural shape of the orientation fields neighboring the core or delta is analyzed. The performance of the proposed method is tested using 27,000 fingerprints of NIST Special Database 14. The results obtained are very encouraging with an accuracy rate of 89.31% that markedly outperformed the latest work

A bisector line field approach to interpolation of orientation fields

We propose an approach to the problem of global reconstruction of an orientation field. The method is based on a geometric model called "bisector line fields", which maps a pair of vector fields to an orientation field, effectively generalizing the notion of doubling phase vector fields. Endowed with a well chosen energy minimization problem, we provide a polynomial interpolation of a target orientation field while bypassing the doubling phase step. The procedure is then illustrated with examples from fingerprint analysis

Reconstruction of fingerprints from minutiae points

Most fingerprint authentication systems utilize minutiae information to compare fingerprint images. During enrollment, the minutiae template of a user\u27s fingerprint is extracted and stored in the database. In this work, we concern ourselves with the amount of fingerprint information that can be elicited from the minutiae template of a user\u27s fingerprint. We demonstrate that minutiae information can reveal substantial details such as the orientation field and class of the (unseen) parent fingerprint that can potentially be used to reconstruct the original fingerprint image.;Given a minutiae template, the proposed method first estimates the orientation map of the parent fingerprint by constructing minutiae triplets. The estimated orientation map is observed to be remarkably consistent with the underlying ridge flow of the unseen parent fingerprint. We also discuss a fingerprint classification technique that utilizes only the minutiae information to determine the class of the fingerprint (Arch, Left loop, Right loop and Whorl). The proposed classifier utilizes various properties of the minutiae distribution such as angular histograms, density, relationship between minutiae pairs, etc. A classification accuracy of 82% is obtained on a subset of the NIST-4 database. This indicates that the seemingly random minutiae distribution of a fingerprint can reveal important class information. (Abstract shortened by UMI.)

Indexing techniques for fingerprint and iris databases

This thesis addresses the problem of biometric indexing in the context of fingerprint and iris databases. In large scale authentication system, the goal is to determine the identity of a subject from a large set of identities. Indexing is a technique to reduce the number of candidate identities to be considered by the identification algorithm. The fingerprint indexing technique (for closed set identification) proposed in this thesis is based on a combination of minutiae and ridge features. Experiments conducted on the FVC2002 and FVC2004 databases indicate that the inclusion of ridge features aids in enhancing indexing performance. The thesis also proposes three techniques for iris indexing (for closed set identification). The first technique is based on iriscodes. The second technique utilizes local binary patterns in the iris texture. The third technique analyzes the iris texture based on a pixel-level difference histogram. The ability to perform indexing at the texture level avoids the computational complexity involved in encoding and is, therefore, more attractive for iris indexing. Experiments on the CASIA 3.0 database suggest the potential of these schemes to index large-scale iris databases

A Review of Wavelet Based Fingerprint Image Retrieval

A digital image is composed of pixels and information about brightness of image and RGB triples are used to encode color information. Image retrieval problem encountered when searching and retrieving images that is relevant to a user’s request from a database. In Content based image retrieval, input goes in the form of an image. In these images, different features are extracted and then the other images from database are retrieved accordingly. Biometric distinguishes the people by their physical or behavioral qualities. Fingerprints are viewed as a standout amongst the most solid for human distinguishment because of their uniqueness and ingenuity. To retrieve fingerprint images on the basis of their textural features,by using different wavelets. From the input fingerprint image, first of all center point area is selected and then its textural features are extracted and stored in database. When a query image comes then again its center point is selected and then its texture feature are extracted. Then these features are matched for similarity and then resultant image is displayed. DOI: 10.17762/ijritcc2321-8169.15026

A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.This work was supported by the Research Projects CAB(CDTI), TIN2011-28488, and TIN2013-40765-P.

A Survey of Fingerprint Classification Part I: Taxonomies on Feature Extraction Methods and Learning Models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.Research Projects CAB(CDTI) TIN2011-28488 TIN2013-40765Spanish Government FPU12/0490