Poor Quality Fingerprint Images Research Articles

Development of an Improved Multi-filtering Matching Model for Fingerprint Recognition

Over the years research done in the area of fingerprint recognition in which the hybrid matching algorithm is one of the most common techniques, though the hybrid algorithm performed well but still faced with the challenge of false minutiae. This study formulated, simulated, and evaluated a multi-filtering fingerprint matching model to develop a multi-filtering matching model for fingerprint recognition. The method employed a multi-filtering model that was formulated using image pre-processing; minutiae feature extraction, post-processing, and cancellation of false minutiae algorithms in the processed images. The model was simulated using Matlab and fingerprint images from the Fingerprint Verification Competition (FVC) 2002 database. The performance of the model was evaluated using the False Acceptance Rate (FAR), False Rejection Rate (FRR), and Error Equal Rate (EER). The results showed that the false minutiae cancellation algorithm considerably reduced the false minutiae points in the thinned images which resulted in the reduction of false acceptance when two different images were tested, and also reduction in false rejection rate when two same images were tested. The match score was below the threshold value of 50 for false acceptance rate and above the threshold value of 50 for the false rejection rate. The error equal rate EER value of 0.076 was recorded. The study concluded that there was a significant reduction in the false minutiae points present in the thinned images and that a high accuracy of fingerprint matching was achieved when the datasets include poor quality fingerprint images.

Fast Fourier transform for fingerprint enhancement and features extraction with adaptive block size using orientation fields

Extracting minutiae from a digital fingerprint is a crucial step in a fingerprint-based recognition systems. This work deals with poor-quality fingerprint images containing broken ridges. The enhancement stage connects broken ridges and is essential for extracting correct minutiae. We use a FFT variant [8] for this stage, but, to truly benefit from FFT in a block, it is essential to determine a suitable block size, depending on ridges orientation field. We propose to use a quadtree to partition the ridges orientation field into homogeneous blocks. A block is homogeneous when at least seventy percent of its ridges orientations are within ten degrees. Another issue addressed in this article is the choice of a suitable neighborhood window size W for computing orientation field image, depending on the fingerprint image quality. The performance improvements of our algorithm are evaluated and compared with standard measures MSE, PSNR and GI, on databases DB1 to DB4 of FVC2004 and NIST special database SD302d.

End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement.

Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the “Automated Deep Convolutional Neural Network (DCNN)” and “Fast Fourier Transform (FFT)” filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an “FFT Enhancement” algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed “Automated Latent Minutiae Extractor (ALME)”. Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed “Frequency Enhanced Minutiae Matcher (FEMM)” algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.

Fingerprint image quality assessment and scoring using minutiae centered local patches

Performance of an automatic fingerprint identification system (AFIS) depends on the quality of fingerprint images. Therefore, quality estimation of fingerprint images can lead to performance enhancement of AFIS by eliminating the poor quality fingerprint images. A fingerprint quality estimation algorithm is proposed, which computes the fingerprint image quality at local level (blockwise). The proposed quality estimation algorithm analyzes blocks of fingerprint images in terms of their quality nature (dry, normal dry, good, normal wet, and wet). The features used for quality nature assessment are moisture, mean, variance, ridge valley area uniformity, and ridge line count. The performance of the block quality nature is assessed on fingerprint verification competition (FVC) 2004 datasets using a decision tree classifier. The proposed approach achieves classification accuracy of 95.90%. Further, the overall quality score (QS) for a fingerprint image is obtained by combining QSs assigned to all minutiae centered local patches of the fingerprint image using quality nature assessment, orientation analysis, and clarity analysis. The overall QSs for fingerprint images of FVC 2004 database (DB1, DB2, DB3, and DB4 datasets) are computed. These scores are used to evaluate the quality ranked recognition performance on each dataset of FVC 2004 database. Experimental evaluations reveal that rejecting low quality fingerprint images improves the performance of the recognition system. A comparative study with state-of-the-art quality estimation algorithms indicates that the QSs assigned using the proposed method are accurate and precise. Therefore, the proposed method can be used as a quality control unit during fingerprint acquisition, which helps in improving the performance of a recognition algorithm.

LQM: Localized Quality Measure for Fingerprint Image Enhancement

Fingerprint is one of the most widely used biometric in law enforcement. However, low-quality fingerprint images can drastically degrade the performance of automated fingerprint identification systems (AFIS). AFIS can be substantially advanced by: 1) establishing a metric to evaluate the image quality accurately and 2) utilizing this metric to enable an automated enhancement process. This paper offers a novel localized quality measure (LQM) to evaluate the quality of fingerprint images, and a genetic localized quality measure enhancement (LQME) algorithm, which is tailored to iteratively enhance poor-quality fingerprint images. In addition, a method is introduced to automatically choose the enhancement algorithm's parameters based on the proposed measure such that it yields the best enhancement result. The presented LQM measure uses fingerprint image characteristics, which include sharpness, contrast, orientation certainty level, symmetry features, and imprints of friction ridge structure (minutiae) information. The FVC2004 Set B database containing fingerprint images from four different sensors and a total of 240 images (80 from each sensor) is used to evaluate the performance of the presented algorithms and methods. The computer simulations demonstrate that the LQM measure is useful in predicting the quality of the fingerprint images captured from various devices. Furthermore, the experiments show that LQME can recover retrievable-corrupt fingerprint regions.

A new method for fingerprint matching using phase-only auto- and cross-bispectrum

Fingerprints are widely used in biometric techniques for automatic personal identification. In this work, we present a new correlation-based approach which uses the phase-only auto- and cross-bispectrum. This paper deals with the problem of aligning two fingerprints images under translation and rotation. The method described utilizes the shift invariance property of the auto-bispectrum to eliminate the effect of the translation component. Only the phase information is preserved from the auto-bispectrum in order to achieve better resilience against non-uniform illumination changes. The input fingerprint image that gives the highest correlation peak is selected as the rotation-normalized image. The theoretical basis for fingerprint matching by auto- and cross-bispectrum analysis is presented. To prove the feasibility of the proposed method, we compared it to a the method proposed by Ito et al. (IEICE Trans Fundam E87-A(3):682–691, 2004) implemented in a similar manner as our approach. The experiments executed on FVC 2004 have shown that the suggested algorithm is successful on the bad or poor quality fingerprint images. The algorithm is robust providing accurate results in most of the time.

Gabor Filter-based Multiple Enrollment Fingerprint Recognition

in the implementation of multiple enrollment fingerprint recognition systems. However, these techniques suffer the difficulty of automatically extracting all minutiae points due to failure to detect the complete ridge structures of a fingerprint. With poor quality fingerprint images, detection of minutiae points as well as describing all the local ridge structures is difficult. It is also difficult to quickly match two fingerprints that have a difference in the number of unregistered minutiae. Non-minutiae based techniques such as Gabor filtering are rich in terms of distinguishing features and can be used as an alternative since they capture both the local and global details in a fingerprint. This paper presents a Gabor filter-based approach; the first of the kind to implement a verification multiple enrollment based fingerprint recognition system. The Gabor filter-based multiple enrollment fingerprint recognition method was compared with a spectral minutiae-based method using two fingerprint databases; FVC 2000-DB2-A and FVC 2006-DB2-A. Although the minutiaebased method outperformed the Gabor filter-based method, the results attained from the later are promising and can be a good basis for implementing Gabor filter-based techniques in designing multiple enrollment based fingerprint systems.

An Efficient Fingerprint Matching System for Low Quality Images

ABSTRACT Fingerprint-based identification is one of the most well-known and publicized biometrics for personal identification. It remains a reliable, efficient and commonly accepted biometric. In this paper, a fingerprint recognition system for identifying the low quality fingerprint images on Myanmar National Registration Cards (NRCs) is developed. Traditional minutia based approach is not robust to poor quality fingerprint images. In proposed system, ridge feature-based approach for fingerprint recognition using contextual filter and single pass thinning algorithm is developed. The input image is preprocessed and gabor filtering is applied for ridge line enhancement. The system extracts the ridge line features from the skeleton image derived from single pass thinning algorithm and it is compared to the database using Euclidean distance metric. The effectiveness of the proposed system can be confirmed through the experimental results.

Online Fingerprint Verification Algorithm and Distributed System

In this paper, a novel online fingerprint verification algorithm and distribution system is proposed. In the beginning, fingerprint acquisition, image preprocessing, and feature extraction are conducted on workstations. Then, the extracted feature is transmitted over the internet. Finally, fingerprint verification is processed on a server through web-based database query. For the fingerprint feature extraction, a template is imposed on the fingerprint image to calculate the type and direction of minutiae. A data structure of the feature set is designed in order to accurately match minutiae features between the testing fingerprint and the references in the database. An elastic structural feature matching algorithm is employed for feature verification. The proposed fingerprint matching algorithm is insensitive to fingerprint image distortion, scale, and rotation. Experimental results demonstrated that the matching algorithm is robust even on poor quality fingerprint images. Clients can remotely use ADO.NET on their workstations to verify the testing fingerprint and manipulate fingerprint feature database on the server through the internet. The proposed system performed well on benchmark fingerprint dataset.

Orientation Field Estimation for Embedded Fingerprint Authentication System

Orientation field (OF) estimation is a fundamental process in fingerprint authentication systems. In this paper, a novel binary pattern based low-cost OF estimation algorithm is proposed. The new method consists of two modules. The first is block-level orientation estimation and averaging in vector space by pixel level orientation statistics. The second is orientation quantization and smoothing. In the second module, the continuous orientation is quantized into fixed orientations with sufficient resolution (interval between fixed orientations). An effective smoothing scheme on the quantized orientation space is also proposed. The proposed algorithm is capable of stably processing poor-quality fingerprint images and is validated by tests conducted on an adaptive OF matching scheme. The proposed algorithm is also implemented into a fingerprint System on Chip (SoC) to comfirm that it satisfies the strict requirements of embedded system.

Robust Orientation Field Estimation and Extrapolation Using Semilocal Line Sensors

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Orientation field (OF) estimation is a crucial preprocessing step in fingerprint image processing. In this paper, we present a novel method for OF estimation that uses traced ridge and valley lines. This approach provides robustness against disturbances caused, e.g., by scars, contamination, moisture, or dryness of the finger. It considers pieces of flow information from a larger region and makes good use of fingerprint inherent properties like continuity of ridge flow perpendicular to the flow. The performance of the line-sensor method is compared with the gradients-based method and a multiscale directional operator. Its robustness is tested in experiments with simulated scar noise which is drawn on top of good quality fingerprint images from the FVC2000 and FVC2002 databases. Finally, the effectiveness of the line-sensor-based approach is demonstrated on 60 naturally poor quality fingerprint images from the FVC2004 database. All orientations marked by a human expert are made available at the journal's and the authors' website for comparative tests. </para>

A WAVELET-BASED APPROACH TO RIDGE THINNING IN FINGERPRINT IMAGES

As a global feature of fingerprints, the thinning of ridges, extraction of minutiae and computation of orientation field are very important for automatic fingerprint recognition. Many algorithms have been proposed for their computation and estimation, but their results are unsatisfactory, especially for poor quality fingerprint images. In this paper, a robust wavelet-based method to create thinned ridge map of fingerprint for automatic recognition is proposed. Properties of modulus minima based on the spline wavelet function are substantially investigated. Desirable characteristics show that this method is suitable to describe the skeleton of the ridge of the fingerprint image. A multi-scale thinning algorithm based on the modulus minima of wavelet transform is presented. The proposed algorithm is able to improve the skeleton representation of the ridge of the fingerprint without side-effects and limitations of the existing methods. The thinned ridge map can facilitate the extraction of the minutiae for matching in fingerprint recognition. Experiments have been conducted to validate the effectiveness and efficiency of the proposed method.

A Model-Based Method for the Computation of Fingerprints' Orientation Field

As a global feature of fingerprints, the orientation field is very important for automatic fingerprint recognition. Many algorithms have been proposed for orientation field estimation, but their results are unsatisfactory, especially for poor quality fingerprint images. In this paper, a model-based method for the computation of orientation field is proposed. First a combination model is established for the representation of the orientation field by conidering its smoothness except for several singular points, in which a polynomial model is used to describe the orientation field globally and a point-charge model is taken to improve the accuracy locally at each singular point. When the coarse field is computed by using the gradient-based algorithm, a further result can be gained by using the model for a weighted approximation. Due to the global approximation, this model-based orientation field estimation algorithm has a robust performance on different fingerprint images. A further experiment shows that the performance of a whole fingerprint recognition system can be improved by applying this algorithm instead of previous orientation estimation methods.

Fingerprint ridge allocation in direct gray-scale domain

Ridges and ravines are the main components constituting a fingerprint. Traditional automatic fingerprint identification systems (AFIS) are based on minutiae matching techniques. The minutiae for fingerprint identification are defined by ridge termination and ridge bifurcation. Most AFIS perform ridge line following process to automatically detect minutiae based on binary or skeleton fingerprint image. For low-quality fingerprint images, the preprocessing stage of an AFIS produces redundant minutiae or even destroys real minutiae. The minutiae detection algorithms in direct gray-scale domain have been developed to overcome these problems. The first step of gray-scale minutiae detection algorithm is to determine ridge locations and then perform gray-scale ridge line following algorithm to extract minutiae. However, the existing gray-scale minutiae detection techniques can only work on partial fingerprint image due to the ignorance of image background. Moreover, the gray value variation inside a ridge also generates redundant ridge points. In this paper, we propose a novel method, based on gray-level histogram decomposition, to locate the ridge points in complete fingerprint images. By decomposing the gray-level histogram, redundant ridge points can be eliminated according to some statistical parameters. Experimental results demonstrate that the correct rate can be over 95% even applied to poor-quality fingerprint images.