Core Point Detection Research Articles

Fingerprint core point detection using connected component approach and orientation map edge tracing approach

Fingerprint core point detection using connected component approach and orientation map edge tracing approach

Fingerprint core point detection using connected component approach and orientation map edge tracing approach

Fingerprint recognition has always played a key role in forensic and commercial applications. The most challenging part of this recognition is to detect the accurate position of the fingerprint's c...

Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection.

Fingerprints have long been used in automated fingerprint identification or verification systems. Singular points (SPs), namely the core and delta point, are the basic features widely used for fingerprint registration, orientation field estimation, and fingerprint classification. In this study, we propose an adaptive method to detect SPs in a fingerprint image. The algorithm consists of three stages. First, an innovative enhancement method based on singular value decomposition is applied to remove the background of the fingerprint image. Second, a blurring detection and boundary segmentation algorithm based on the innovative image enhancement is proposed to detect the region of impression. Finally, an adaptive method based on wavelet extrema and the Henry system for core point detection is proposed. Experiments conducted using the FVC2002 DB1 and DB2 databases prove that our method can detect SPs reliably.

An Improved Approach in Core Point Detection Algorithm for Fingerprint Recognition

Core Point Detection is used when comparison of two fingerprint based on alignment is done. To extract the core point is still open issue in fingerprint recognition. The success rate of fingerprint recognition as well as to speed up the process core point is used. The proposed core point detection algorithm follows four steps: normalization, ridge orientation estimation, smoothing and core point detection. To test the experimental result and performance of proposed algorithm carried out using FVC2000 and FingerDOS databases and prove that, the FMR and FNMR for FVC2000 is 2 and 1.2 and for FingerDOS is 0 and 1.67 respectively. The accuracy of FVC2000 is 98.4% and FingerDOS is 99.16%. The average execution time of FVC2000 is less than 0.72 and for FingerDOS is 0.25 second.

Preventing Cloud Attacks using Bio-Metric Authentication in Cloud Computing

Objectives: To provide a secured and efficient solution for end users to access their personal files in the cloud servers using biometric authentication. Methods/: In this Minutiae Map (MM) algorithm is implemented for processing fingerprint based authentication. The user personal files are stored in free public multiple cloud storages namely Dropbox and CloudMe using splitting and merging techniques. RC4 algorithm is used to improve the security in cloud environment. Cross site request forgery (CSRF) and Cross site scripting (XSS) prevention techniques are used to provide security against cloud attacks. Findings: This study analyses that MM algorithm is the best accurate fingerprint feature extraction algorithm compared to Orientation Map, Gabor Filter and core point detection techniques. The proposed approach measures the user personal files upload time in cloud servers namely Dropbox and CloudMe. The study also analyses the presence of CSRF and XSS attacks in the application. Applications/ Improvements: The proposed system can be improvised involving preventive measures for more security threats and integrating other biometric authentications.

Reference point detection for camera-based fingerprint image based on wavelet transformation.

BackgroundFingerprint recognition systems essentially require core-point detection prior to fingerprint matching. The core-point is used as a reference point to align the fingerprint with a template database. When processing a larger fingerprint database, it is necessary to consider the core-point during feature extraction. Numerous core-point detection methods are available and have been reported in the literature. However, these methods are generally applied to scanner-based images. Hence, this paper attempts to explore the feasibility of applying a core-point detection method to a fingerprint image obtained using a camera phone.MethodThe proposed method utilizes a discrete wavelet transform to extract the ridge information from a color image. The performance of proposed method is evaluated in terms of accuracy and consistency. These two indicators are calculated automatically by comparing the method’s output with the defined core points.ResultsThe proposed method is tested on two data sets, controlled and uncontrolled environment, collected from 13 different subjects. In the controlled environment, the proposed method achieved a detection rate 82.98%. In uncontrolled environment, the proposed method yield a detection rate of 78.21%.ConclusionThe proposed method yields promising results in a collected-image database. Moreover, the proposed method outperformed compare to existing method.

Scale Invariant Feature Transform Based Fingerprint Corepoint Detection

The detection of singular points (core and delta) accurately and reliably is very important for classification and matching of fingerprints. This paper presents a new approach for core point detection based on scale invariant feature transform (SIFT). Firstly, SIFT points are extracted ,then reliability and ridge frequency criteria are applied to reduce the candidate points required to make a decision on the core point. Finally a suitable mask is applied to detect an accurate core point. Experiments on FVC2002 and FVC2004 databases show that our approach locates a unique reference point with high accuracy. Results of our approach are compared with those of the existing methods in terms of accuracy of core point detection. Defence Science Journal, 2013, 63(4), pp.402 -407 , DOI:http://dx.doi.org/10.14429/dsj.63.2708

Fast and accurate algorithm for core point detection in fingerprint images

The core point is used to align between the fingerprints in the fingerprint authentication systems faster than the conventional techniques. To speed up the processing for the real time applications, it is more convenient to implement the image processing algorithms using embedded modules that can be used in the portable systems. To do this, the algorithm should be characterized by a simple design for easier and more feasible implementation on the embedded modules. The proposed work, in this paper, presents a mask that locates the core point simply from the ridge orientation map. The introduced algorithm detects the core point at the end of the discontinuous line appearing in the orientation map presented by a gray-scale. A property is presented and supported with a mathematical proof to verify that the singular regions are located at the end of this discontinuous line. The experimental results, on the public FVC2002 and FVC2004 databases, show that the proposed mask exhibits an average increase in the correct core point detection per fingerprint by 17.35%, with a reduction in the false detection by 51.23%, compared to a fast edge-map based method. Moreover, the execution time is reduced by an average factor of 1.8.

Touch-less Fingerprint Analysis — A Review and Comparison

Touch-less fingerprint recognition system is a reliable alternative to conventional touch-based fingerprint recognition system. Touch-less system is different from conventional system in the sense that they make use of digital camera to acquire the fingerprint image where as conventional system uses live-acquisition techniques. The conventional fingerprint systems are simple but they suffer from various problems such as hygienic, maintenance and latent fingerprints. In this paper we present a review of touch-less fingerprint recognition systems that use digital camera. We present some challenging problems that occur while developing the touch-less system. These problems are low contrast between the ridge and the valley pattern on fingerprint image, non-uniform lighting, motion blurriness and defocus, due to less depth of field of digital camera. The touch-less fingerprint recognition system can be divided into three main modules: preprocessing, feature extraction and matching. Preprocessing is an important step prior to fingerprint feature extraction and matching. In this paper we put our more emphasis on preprocessing so that the drawbacks stated earlier can be removed. Further preprocessing is divided into four parts: first is normalization, second is fingerprint Segmentation, third is fingerprint enhancement and last is the core point detection. Feature extraction can be done by Gabor filter or by minutia extraction and the matching can be done by Support Vector Machine or Principal Component Analysis and three distance method.

Fingerprint Core Point Detection based on SVM and Complex Filter

Fingerprint Core Point Detection based on SVM and Complex Filter

Fingerprint’s Core Point Detection using Gradient Field Mask

Fingerprint recognition is a widely used biometric identification mechanism. In case of correlation based fingerprint recognition detection of a consistent registration point is a crucial issue; this point can be a core point of a fingerprint. Many techniques have been proposed but success rate is highly dependent on input used and accurate core point detection is still an open issue. Here we discuss a core point detection algorithm which is computationally simple and gives consistent detection of a core point. The proposed technique is based on orientation field of the fingerprint which is calculated using gradient based technique and optimized neighborhood averaging to generate a smoother orientation field, on which we operate a specially designed mask for detecting core point as the orientation field in the region of the core point is different than the other area. Though all fingerprints don’t possess core point still this algorithm is useful to detect high curvature regions. This algorithm is helpful in the development of correlation based Automatic Fingerprint Recognition System (AFIS).

Fingerprint Core Point Detection Algorithm using Orientation Field Based Multiple Features

Fingerprint recognition is a widely used biometric identification mechanism. In case of correlation based fingerprint recognition detection of a consistent registration point is a crucial issue; this point can be a core point of a fingerprint. Many techniques have been proposed but success rate is highly dependent on input used and accurate core point detection is still an open issue. Here we discuss an algorithm which is based on multiple features derived from the fingerprint which are collectively used for consistent core point detection. Here we use Orientation field, coherence, Poincare index for core point detection. Though all fingerprints don’t possess core point still this algorithm is useful to detect high curvature regions and gives high accuracy as it combines advantages form individual features. This algorithm is crucial in the development of correlation based Automatic Fingerprint Recognition System (AFIS).

An efficient algorithm for fingerprint preprocessing and feature extraction

In practice, the placement of finger on the scanner for authentication is not done with the utmost care as when placed during the enrollment and this result in rejections of genuine users. Moreover, user behaviour and envionmental conditions decrease the geniune acceptance rate (GAR) for authentication of fingerprints. To overcome these limitations, an efficient preprocessing algorithm is proposed to achieve good vertical orientation and high ridge curvature area around the core point for fingerprint authentication and analysis. The algorithm is implemented in two stages. The process of obtaining the vertical oriented fingerprint image is carried out in the first step. This is followed by core point detection of a fingerprint. Core point detection is efficiently identified for any type of fingerprints. The developed algorithm is tested using a line based feature extraction algorithm with a large internal database and samples of fingerprint vericication competition (FVC). Only for the poor quality images, broken ridges are identified which results in a difference in minutiae points. With the proposed algorithm 94% of the tested images were oriented vertically and its genuiness is verified by comparing the minutiae details of the oriented and unoriented image of the same subject.

Errata: Core-point detection based on edge maps in fingerprint images

This PDF file contains the errata for “JEI Vol. 18 Issue 02 Paper JEI ERR-APR2009-1” for JEI Vol. 18 Issue 02

Core-point detection based on edge maps in fingerprint images

We propose a novel and accurate technique based on edge map for the determination of core points in fingerprint images. An edge map is obtained by detecting the edges on a smoothed orientation map. An edge pixel deletion operation is then performed on the edge map based on gradient information. Last, upper and lower core points can be extracted by analyzing the orientation consistency of a few edge pixels. Experimental results show that the proposed method can effectively detect the core points with high speed for all types of fingerprints.

Upper core point detection using improved ant colony optimization algorithm

Minutiae detection is a crucial process in an automatic fingerprint identification system. Most fingerprint comparison algorithms are based on minutiae matching. However, the local orientation changes very rapidly in the singular point area. It is difficult to locate the singular point precisely. The Ant Colony Optimization Algorithm (ACOA) is extensively used in multi-objective and optimal problems. But the ACOA is still not used in fingerprint image processing. In this paper, we suggest an improved Ant Colony Optimization Algorithm to extract the upper core point of fingerprints. Finally, the proposed algorithms are tested with some fingerprint images and show significant improvement in the experiments.

Orientation- and edge-map-based fingerprint core-point detection

A new core-point detection method using orientation and edge maps, where a color loop-up table is defined and used to obtain the orientation map, is presented. To achieve high speed detection, all processes are performed with the reduced image. The edge map is obtained by detecting the edges on the orientation map. Then an upper-part maximum edge region is defined as the edge region with maximum area in the edge map. Finally, the core point can be detected simply by finding a point located in the lowest part of the upper-part maximum edge region. Experimental results show that the proposed method can effectively detect the core point for all types of fingerprints including the arch type.

Nonminutiae-Based Decision-Level Fusion for Fingerprint Verification

Most of the proposed methods used for fingerprint verification are based on local visible features called minutiae. However, due to problems for extracting minutiae from low-quality fingerprint images, other discriminatory information has been considered. In this paper, the idea of decision-level fusion of orientation, texture, and spectral features of fingerprint image is proposed. At first, a value is assigned to the similarity of block orientation field of two-fingerprint images. This is also performed for texture and spectral features. Each one of the proposed similarity measure does not need core-point existence and detection. Rotation and translation of two fingerprint images are also taken into account in each method and all points of fingerprint image are employed in feature extraction. Then, the similarity of each feature is normalized and used for decision-level fusion of fingerprint information. The experimental results on FVC2000 database demonstrate the effectiveness of the proposed fusion method and its significant accuracy.

A new approach to reference point location in fingerprint recognition

Fingerprint matching is one of the most important problems in Fingerprint Identification System (AFIS). In this paper a new method of the reference point alignment has been presented. A new approach of reference point localization is based on so-called identification masks which have been composed on the basis of analysis of biometric characteristic of human finger. Construction of such masks has been presented.Experiments show that our approach locates a unique reference point with high accuracy for all types of fingerprints. Generally, fingerprint matching consists with three steps: core (reference) point detection, filter the image using a bank Gabor filters, and comparison with imprint pattern. It seems, that today, the Gabor filtering gives the best results in fingerprint recognition. The proposed method was evaluated and tested on various fingerprint images, included in the FVC2000 fingerprint database. Performed results with representative investigations have been compared.