390 research outputs found
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 Novel Technique for Fingerprint Classification based on Fuzzy C-Means and Naive Bayes Classifier
Fingerprint classification is a key issue in
automatic fingerprint identification systems. One of the main
goals is to reduce the item search time within the fingerprint
database without affecting the accuracy rate. In this paper, a
novel technique, based on topological information, for
efficient fingerprint classification is described. The proposed
system is composed of two independent modules: the former
module, based on Fuzzy C-Means, extracts the best set of
training images; the latter module, based on Fuzzy C-Means
and Naive Bayes classifier, assigns a class to each processed
fingerprint using only directional image information. The
proposed approach does not require any image enhancement
phase. Experimental trials, conducted on a subset of the free
downloadable PolyU database, show a classification rate of
91% over a 100 images test database using only 12 training
examples
Core Point Pixel-Level Localization by Fingerprint Features in Spatial Domain
Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples
An Advanced Technique for User Identification Using Partial Fingerprint
User identification is a very interesting and
complex task. Invasive biometrics is based on traits
uniqueness and immutability over time. In forensic field,
fingerprints have always been considered an essential
element for personal recognition. The traditional issue is
focused on full fingerprint images matching. In this paper an
advanced technique for personal recognition based on
partial fingerprint is proposed. This system is based on
fingerprint local analysis and micro-features, endpoints and
bifurcations, extraction. The proposed approach starts from
minutiae extraction from a partial fingerprint image and
ends with the final matching score between fingerprint pairs.
The computation of likelihood ratios in fingerprint
identification is computed by trying every possible
overlapping of the partial image with complete image. The
first experimental results conducted on the PolyU (Hong
Kong Polytechnic University) free database show an
encouraging performance in terms of identification
accuracy
An Efficient Reconfigurable Architecture for Fingerprint Recognition
The fingerprint identification is an efficient biometric technique to authenticate human beings in real-time Big Data Analytics. In this paper, we propose an efficient Finite State Machine (FSM) based reconfigurable architecture for fingerprint recognition. The fingerprint image is resized, and Compound Linear Binary Pattern (CLBP) is applied on fingerprint, followed by histogram to obtain histogram CLBP features. Discrete Wavelet Transform (DWT) Level 2 features are obtained by the same methodology. The novel matching score of CLBP is computed using histogram CLBP features of test image and fingerprint images in the database. Similarly, the DWT matching score is computed using DWT features of test image and fingerprint images in the database. Further, the matching scores of CLBP and DWT are fused with arithmetic equation using improvement factor. The performance parameters such as TSR (Total Success Rate), FAR (False Acceptance Rate), and FRR (False Rejection Rate) are computed using fusion scores with correlation matching technique for FVC2004 DB3 Database. The proposed fusion based VLSI architecture is synthesized on Virtex xc5vlx30T-3 FPGA board using Finite State Machine resulting in optimized parameters
Novel Feature Extraction Methodology with Evaluation in Artificial Neural Networks Based Fingerprint Recognition System
Fingerprint recognition is one of the most common biometric recognition systems that includes feature extraction and decision modules. In this work, these modules are achieved via artificial neural networks and image processing operations. The aim of the work is to define a new method that requires less computational load and storage capacity, can be an alternative to existing methods, has high fault tolerance, convenient for fraud measures, and is suitable for development. In order to extract the feature points called minutia points of each fingerprint sample, Multilayer Perceptron algorithm is used. Furthermore, the center of the fingerprint is also determined using an improved orientation map. The proposed method gives approximate position information of minutiae points with respect to the core point using a fairly simple, orientation map-based method that provides ease of operation, but with the use of artificial neurons with high fault tolerance, this method has been turned to an advantage. After feature extraction, General Regression Neural Network is used for identification. The system algorithm is evaluated in UPEK and FVC2000 database. The accuracies without rejection of bad images for the database are 95.57% and 91.38% for UPEK and FVC2000 respectively
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
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.
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