Fingerprint Matching Algorithm Research Articles

A UWB indoor positioning method based on triple filtering fingerprint matching algorithm

Abstract In recent years, ultra-wideband (UWB) has gradually become a research hot spot in the field of indoor positioning because of its various advantages. Although UWB has such excellent performance in normal environments, the non-line-of-sight propagation of signals in complex indoor environments and the multi-path effect caused by obstacles will affect its positioning accuracy. To solve this problem, we use the fingerprint positioning method and optimize the previously commonly used k-nearest neighbor algorithm in the online matching phase. In this paper, we proposed a triple-filtered k-nearest neighbor algorithm based on sample distance weighting (TFWKNN). Experimental results show that the mean calculation error of TFWKNN is 63.4% less than the k-nearest algorithm, and the proposed algorithm has better prediction stability than other commonly used machine learning regression algorithms.

DOA method based on CSI and improved neural network

Abstract In wireless communication systems, accurate estimation of signal direction has always been a key research area. Channel State Information (CSI) is an important parameter that can be used to estimate the Direction of Arrival (DOA) of a signal. In order to improve the accuracy and robustness of DOA estimation, this study aims to explore a DOA estimation method based on CSI and improved neural networks. Firstly, the role of CSI in localization technology and related theories are detailed. Subsequently, the application of neural network-based fingerprint matching algorithms, including BP neural networks, Whale Optimization Algorithm (WOA), and chaotic mapping techniques, is analyzed. In light of this, an improved BP neural network integrating chaotic mapping and adaptive weighting is proposed to improve the positioning accuracy and convergence rate of the algorithm. Finally, through the collection and training of experimental data, an algorithm proposed in this article achieves a test set accuracy of 96.4286% and a training set accuracy of 82.7243%. The better training effect is obtained, which verifies the efficacy and superiority of the proposed approach.

Efficient Distorted Fingerprint Matching using OpenCV, and SIFT-FLANN Algorithm

Abstract: This paper introduces an enhanced approach for distorted fingerprint matching, integrating OpenCV, SIFT analysis, and FLANN-based algorithms. By leveraging the robustness of SIFT in feature extraction and the efficiency of FLANN in nearest neighbor matching, our method aims to enhance accuracy and robustness in fingerprint recognition. Experimental evaluations on a dataset of distorted fingerprints demonstrate superior performance compared to some of the existing techniques, showcasing resilience against various distortions such as noise, rotation, and occlusion. Our method offers a reliable solution for real-world applications in security, forensics, and biometric authentication systems. In comparison to recent techniques, our approach significantly improves matching accuracy and computational efficiency, addressing the challenges of distorted fingerprint recognition effectively in modern contexts.

PCA-Enhanced Methodology for the Identification of Partial Discharge Locations

Partial discharge (PD) that occurs due to insulation breakdown is a precursor to plant failure. PD emits electromagnetic pulses which radiate through space and can be detected using appropriate sensing devices. This paper proposed an enhanced radiolocation technique to locate PD. This approach depends on sensing the radio frequency spectrum and the extraction of PD location features from PD signals. We hypothesize that the statistical characterization of the received PD signals generates many features that represent distinct PD locations within a substation. It is assumed that the waveform of the received signal is altered due to attenuation and distortion during propagation. A methodology for the identification of PD locations based on extracted signal features has been developed using a fingerprint matching algorithm. First, the original extracted signal features are used as inputs to the algorithm. Secondly, Principal Component Analysis (PCA) is used to improve PD localization accuracy by transforming the original extracted features into s new informative feature subspace (principal components) with reduced dimensionality. The few selected PCs are then used as inputs into the algorithm to develop a new PD localization model. This work has established that PCA can provide robust PC representative features with spatially distinctive patterns, a prerequisite for a good fingerprinting localization model. The results indicate that the location of a discharge can be determined from the selected PCs with improved localization accuracy compared to using the original extracted PD features directly.

Quantum fingerprinting for heterogeneous devices localization

In this paper, we present QHFP, a device-independent quantum fingerprint matching algorithm that addresses two of the issues for realizing worldwide ubiquitous large-scale location tracking systems: scalability of storage space and running time as well as devices heterogeneity. In particular, we present a quantum algorithm with a complexity that is exponentially better than the classical techniques, both in space and running time. QHFP also has provisions for handling the inherent localization error due to building the large-scale fingerprint using heterogeneous devices. We give the details of the entire system starting from extracting device-independent features from the raw received signal strength, mapping the classical feature vectors to their quantum counterparts, and showing a quantum cosine similarity algorithm for fingerprint matching.We implement our quantum algorithm and deploy it in a real testbed using the IBM Quantum machine simulator. We also study the effect of the different quantum error sources, such as quantum gates error and quantum measurement error, on the system accuracy. Based on this, we discuss the suitable provisions to mitigate the effect of the quantum error sources on the localization accuracy.Results confirm the ability of QHFP to obtain the correct estimated location with an exponential improvement in space and running time compared to the traditional classical counterparts. In addition, the proposed device-independent features lead to more than 20% better accuracy in median error. This highlights the promise of our algorithm for future ubiquitous large-scale worldwide device-independent fingerprinting localization systems.

5G ultra-dense network fingerprint positioning method based on matrix completion

The problem of high-precision indoor positioning in the 5G era has attracted more and more attention. A fingerprint location method based on matrix completion (MC-FPL) is proposed for 5G ultradense networks to overcome the high costs of traditional fingerprint database construction and matching algorithms. First, a partial fingerprint database constructed and the accelerated proximal gradient algorithm is used to fill the partial fingerprint database to construct a full fingerprint database. Second, a fingerprint database division method based on the strongest received signal strength indicator is proposed, which divides the original fingerprint database into several sub-fingerprint databases. Finally, a classification weighted K-nearest neighbor fingerprint matching algorithm is proposed. The estimated coordinates of the point to be located can be obtained by fingerprint matching in a sub-fingerprint database. The simulation results show that the MC-FPL algorithm can reduce the complexity of database construction and fingerprint matching and has higher positioning accuracy compared with the traditional fingerprint algorithm.

A hierarchical heterogeneous ant colony optimization based fingerprint recognition system

Personal identification is crucial to secure data against cyber-attacks. With increasing identity theft, fingerprint recognition systems have a growing importance in enforcing security and reliable identification. Although, most fingerprint recognitions systems use minutiae features for fingerprint matching, they require extensive pre-processing of fingerprints when the image quality is poor. This may introduce false ridge patterns, degrading the performance of the system. Moreover, fingerprint matching over a large database can be inefficient due to high computation time of fingerprint matching algorithms. This demand for fingerprint recognition systems that are fast and reliable. This paper proposes a computationally intelligent fingerprint recognition system that extracts ridge patterns from the fingerprint for matching. Hierarchical Heterogeneous Ant Colony Optimization based Fingerprint Matching (HHACOFM) algorithm has ant agents at different levels in the hierarchy to find a match between the input and stored ridge patterns. The algorithm was evaluated over four databases: a synthetic database generated using SFinGe tool, an internal database, SOCOFing database and FVC2004 database. Experimental results indicate that the proposal achieves high recognition rate compared to the existing approaches. HHACOFM algorithm achieves less EER than the state-of-art approaches. The results were validated using statistical tests. HHACOFM enables parallelism and thus reduces the response time. The proposal is scalable and suitable for real time applications demanding fast fingerprint verification.

Detecting Locally, Patching Globally: An End-to-End Framework for High Speed and Accurate Detection of Fingerprint Minutiae

Billions of fingerprint images are acquired and matched to protect the national borders and in a range of egovernance applications. Fast and accurate minutiae detection from fingerprint images is the key to advance fingerprint matching algorithms for large-scale applications. However, currently available fingerprint minutiae extraction methods are not accurate and fast enough to support such large-scale applications. This paper proposes a new method that uses a lightweight pixelwise local dilated neural network to extract local features and a patch-wise global neural network to recover the global features. It consolidates the local and global fingerprint features to generate a full-size minutiae location map, and then accurately localizes the minutiae positions by using a recursive connected components algorithm. We design a new loss function to accurately detect minutia orientation and incorporate a dynamic end-to-end loss to provide effective supervision in learning discriminant features. It is due to the proposed design and loss function that can enable higher accuracy with significantly less computations. We present reproducible experimental results from five publicly available contact-based and contactless databases that indicate significant improvement in the minutiae detection accuracy, which also leads to enhanced fingerprint matching accuracy. Since the minutiae represent key points in the fingerprint images, the proposed end-to-end minutiae detection method also has a potential to be employed in many other key points detection tasks.

Cluster-Based JRPCA Algorithm for Wi-Fi Fingerprint Localization

Indoor localization services are emerging as an important application of the Internet of Things, which drives the development of related technologies in indoor scenarios. In recent years, various localization algorithms for different indoor scenarios have been proposed. The indoor localization algorithm based on fingerprints has attracted much attention due to its good performance without extra hardware devices. However, the occurrence of fingerprint mismatching caused by the complexity and variability of indoor scenarios is unneglectable, which degrades localization accuracy. In this article, by combining weighted kernel norm and L2,1-norm, a joint-norm robust principal component analysis (JRPCA in brief) assisted indoor localization algorithm is proposed, which can improve the localization accuracy through aggregating the reference points (RPs) and conducting robust feature extraction based on clustering. More specifically, a one-way hierarchical clustering termination method is proposed to obtain reasonable RP clusters adaptively according to the preset RPs. A two-phase fingerprint matching algorithm of JRPCA based on clustering is proposed to further increase the difference between similar RPs, thus facilitating rapid identification and reinforcing localization accuracy. To validate the proposed algorithm, extensive experiments are conducted in real indoor scenarios. The experimental results confirm that the proposed cluster-based JRPCA algorithm outperforms other existing algorithms in terms of robustness and accuracy.

Fingerprint matching using the onion peeling approach and turning function

Fingerprint, as one of the most popular and robust biometric traits, can be used in automatic identification and verification systems to identify individuals. Fingerprint matching is a vital and challenging issue in fingerprint recognition systems. Most fingerprint matching algorithms are minutiae-based. The minutiae points are the ways that the fingerprint ridges can be discontinuous. Ridge ending and ridge bifurcation are two frequently used minutiae in most fingerprint matching algorithms. This article presents a new minutiae-based fingerprint matching using the onion peeling approach. In the proposed method, fingerprints are aligned to find the matched minutiae points. Then, the nested convex polygons of matched minutiae points are constructed and the comparison between peer-to-peer polygons is performed by the turning function distance. Simplicity, accuracy, and low time complexity of the onion peeling approach are three important factors that make it a standard method for fingerprint matching purposes. The performance of the proposed algorithm is evaluated on the database FVC2002. Since the fingerprints that the difference between the number of their layers is more than 2 and the a minutiae matching score lower than 0.15 are ignored, better results are obtained. KeywordsFingerprint Matching, Minutiae, Convex Layers, Turning Function, Computational Geometry.

A Boundary Aware WiFi Localization Scheme Based on UMAP and KNN

In this letter, a novel localization scheme is reported to improve WiFi fingerprint-based localization via dimensionality reduction and an adaptive fingerprint matching algorithm. Specifically, an experimental analysis is firstly carried out to investigate the quality of radio maps. Then, the advanced uniform manifold approximation and projection (UMAP) algorithm is applied to refine the mapping relations from a fingerprint space to a location space in a supervised dimensionality reduction approach, which significantly improves the quality of radio maps. In addition, to further enhance the localization accuracy, boundary aware KNN (B-KNN) is proposed to adaptively deal with fingerprint matching according to inner area or boundary area. Extensive experiments are conducted in a real scenario with the area of nearly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1000~m^{2}$ </tex-math></inline-formula> , and a thorough comparison with several existing popular methods illustrates that the proposed scheme can significantly improve the localization accuracy on average by 33.3%.

Graph Optimization Model Fusing BLE Ranging with Wi-Fi Fingerprint for Indoor Positioning.

To improve the user’s positioning accuracy of a Wi-Fi fingerprint-based positioning algorithm, this study proposes a graph optimization model based on the framework of o that fuses a Wi-Fi fingerprint and Bluetooth Low Energy (BLE) ranging technologies. In our model, the improvement in positioning can be formulated as a nonlinear least-squares optimization problem that a graph can represent. The graph regards users as nodes and our self-designed error functions between users as edges. In the graph, the nodes obtain the initial coordinates through Wi-Fi fingerprint positioning, and all error functions aggregate to a total error function to be solved. To improve the solution effect of the total error function and weaken the influence of measurement error, an information matrix, an edge selection principle, and a Huber kernel function are introduced. The Levenberg–Marquardt (LM) algorithm is used to solve the total error function and the affine transformation estimation is used for the drifting solution. Through experiments, the influence of the threshold in the Huber kernel function is explored, the relationship between the number of nodes in the graph and the optimization effect is analyzed, and the impact of the distribution of nodes is researched. The experimental results show improvements in the positioning accuracy of four common Wi-Fi fingerprint-matching algorithms: KNN, WKNN, GK, and Stg.

Magnetic Matching-Aided Indoor Localization System Based on a Waist-Mounted Self-Contained Sensor Array

This paper presents a magnetic matching-aided indoor localization system based on a waist-mounted self-contained sensor array. Our purpose is to localize and track the elderly in nursing homes through the proposed wearable device to ensure their safety. The device consists of a low-cost 9-axis self-contained sensor array, a microcontroller, and a WiFi transmission module. This system uses the step length and heading-based pedestrian dead reckoning (PDR) framework as the backbone to estimate the user’s position using the averaged inertial data from the sensor array. A magnetic fingerprint matching (MFM) algorithm is introduced to constrain the drift of the PDR system. Meanwhile, we construct a single-step-based hybrid magnetic fingerprint model to improve the low discernibility of the magnetic field. Finally, we propose an augmented particle filter to fuse the PDR and the MFM algorithms to enhance the system performance further. Experimental results show that 95% of the positioning error after fusion is about 1.47 m, which is 99.3% higher than that of PDR, and the average positioning error after fusion is 0.55 m, which is 61.3% higher than that of PDR. Experimental results have successfully validated the effectiveness and high performance of the proposed magnetic matching-aided wearable indoor localization system.

Small-area Fingerprint Recognition Based on Improved ORB Algorithm in Embedded Environment

Most of the fingerprint matching algorithms were proposed for large area fingerprints, which can hardly work effectively in small-area fingerprints. In this work, an improved ORB algorithm is proposed for small-area fingerprint matching in embedded mobile devices. In feature descriptor design, we analyzed the characters of the fingerprint in the embedded mobile devices and discard the multi-scale feature process to reduce the amount of operations. Moreover, we proposed a fusion descriptor combing LBP and rBRIEF descriptor. In the key point matching process, we proposed a two-step (coarse and fine) matching method by using Hamming distance and cosine similarity, respectively. The experimental results show that the proposed method has a rejection rate of 6.4%, a false recognition rate of 0.1%, and an average matching time of 58ms. It can effectively improve the performance of small-area fingerprint matching and meet the application requirements of embedded mobile device authentication.

Latent Fingerprint Indexing: Robust Representation and Adaptive Candidate List

Efficiently identifying the mated gallery fingerprint of a latent fingerprint in a large database requires a highly accurate and efficient fingerprint matching algorithm. The common strategy to achieve this goal is to combine an efficient indexing algorithm with a slow but accurate matching algorithm. Despite of the importance of latent indexing, it has received far less attention than rolled and plain fingerprint indexing. Due to the small fingerprint area, poor image quality and huge variety in information quantity of latent fingerprints, existing rolled and plain fingerprint indexing approaches cannot be simply migrated to the latent fingerprint indexing. In this paper, we propose (1) a multi-scale fixed-length representation approach for latent fingerprint indexing, and (2) a fingerprint information quantity estimation approach for adaptive candidate list reduction. The representation scheme is designed to deal with small finger area and low image quality of latents. The information quantity of a latent is a predictor of the indexing score of its mated gallery fingerprint and thus can be used to determine a proper threshold for its candidate list. Extensive experimental results on NIST SD27, MOLF, N2N, and Hisign latent fingerprint databases show that the proposed method achieved the state-of-the-art indexing accuracy on latent fingerprints, and significantly improved the efficiency of state-of-the-art latent matching algorithm.

Query2Set: Single-to-Multiple Partial Fingerprint Recognition Based on Attention Mechanism

Currently, fingerprint authentication systems in mobile devices, which have limited-size fingerprint sensors, are mainly based on partial fingerprint matching algorithms. To cover all areas of the finger, the system usually collects multiple partially overlapping partial fingerprints during the enrollment. Existing recognition methods either perform score-level fusion after single-to-single matching, or perform single-to-single matching after image-level mosaicking. However, these two-stage methods have the risk of discarding some real information or introducing some fake information. In this paper, we define this &#x201C;query2set&#x201D; task and propose a novel single-to-multiple partial fingerprint recognition method based on atttention mechanism. Our end-to-end deep model can adaptively extract and fuse appropriate features from a set of fingerprints for matching based on the input query fingerprint. Experiments indicate that our method outperforms several state-of-the-art single-to-single approaches and provides a new insight of fingerprint recognition on mobile devices.

Hough Transform for Distinctive Edge Detection to Images in Fingerprint Recognition Matching Transformation

Biometric Identification is a globally renowned procedure, which has been utilised to achieve a successful and accurate level of identification. In the sea of biometrics, fingerprints are deemed more popular when it comes to verification. This results from the presence of the ridges on the fingerprints that are completely exclusive to each individual. Besides that, fingerprints are expansively employed to ascertain and authenticate people individually. Therefore, this study had proposed to employ distinctive Edge Detection techniques together with the Hough Transform to match the images of the fingerprints in a fingerprint matching system. The Hough Transform is a superior procedure carried out to get an accurate series of finer points or lines. The finer points or lines would then distinguish the fingerprints. Nevertheless, it was still a challenge to extract finer points or lines from the fingerprints under uninhibited conditions. Therefore, this paper was organised based on four distinctive steps. First, different Edge Detection operators were employed to perform the fingerprint matching algorithm. Next, the fingerprint matching algorithm was applied twice to the same Edge Detection operators. Thirdly, the Edge Detection operators had been substituted with the Transformation Method for the same matching procedure. For example, the proposed fingerprint matching algorithm comprised of the Hough Transform and same Edge Detection operators. Finally, distinct Edge Detection operators based on the decision making algorithm were used to calculate and determine the percentage of matching. Therefore, this study proved that the prints obtained via the Prewitt Edge Detection together with Hough Transform were in an agreement.

High precision indoor positioning algorithm of single LED lamp based on A-Bayes

With the rapid development of information technology, the application field of city positioning technology is more and more extensive. Traditional indoor positioning technology has shortcomings in positioning accuracy, equipment cost, security and so on. Visible light indoor positioning technology can effectively solve the above problems. This paper presents an Adaptive boosting- Bayes (A-Bayes) localization algorithm. In this algorithm, three tilted photodetectors and one horizontal PD are used as receivers to measure the received optical power of each PD and set up a fingerprint database. The Adaboost strong classifier is used to divide the test space into two regions: the center and the edge. In the online positioning stage, for the measured received optical power, the region to which it belongs is first determined, and then the Bayesian matching algorithm is used to match the regions to complete the positioning. The experimental results show that in the test space of, and the overall average positioning error is 9.27 cm. Compared with the traditional indoor positioning algorithm, the positioning error of the proposed algorithm is smaller; Compared with the traditional multi lamp location algorithm, the algorithm proposed in this paper avoids the interference between multi light source symbols, and the equipment is simpler; Compared with the traditional fingerprint matching algorithm, the proposed algorithm has faster positioning speed.

Efficient and Privacy-Preserving Online Fingerprint Authentication Scheme over Outsourced Data

With the pervasiveness of mobile devices and the development of biometric technology, biometric identification, which can achieve individual authentication relies on personal biological or behavioral characteristics, has attracted widely considerable interest. However, privacy issues of biometric data bring out increasing concerns due to the highly sensitivity of biometric data. Aiming at this challenge, in this paper, we present a novel privacy-preserving online fingerprint authentication scheme, named e-Finga, over encrypted outsourced data. In the proposed e-Finga scheme, the user's fingerprint registered in trust authority can be outsourced to different servers with user's authorization, and secure, accurate and efficient authentication service can be provided without the leakage of fingerprint information. Specifically, an improved homomorphic encryption technology for secure euclidean distance calculation to achieve an efficient online fingerprint matching algorithm over encrypted FingerCode data in the outsourcing scenarios. Through detailed security analysis, we show that e-Finga can resist various security threats. In addition, we implement e-Finga over a workstation with a real fingerprint database, and extensive simulation results demonstrate that the proposed e-Finga scheme can serve efficient and accurate online fingerprint authentication.

Fingerprint feature data matching algorithm based on distributed computing

In order to effectively reduce the running time of fingerprint feature data matching algorithm and improve the matching accuracy. This paper designs and proposes a fingerprint feature data equivalent matching algorithm based on distributed computing. In the process of de-counterfeiting, the method of combining distance de-counterfeiting with structure de-counterfeiting is adopted. The information of minutiae feature points for subsequent matching, sampling information of corresponding ridge and direction of minutiae points are obtained by ridge tracking method. On this basis, use the fingerprint matching algorithm based on the congruent triangle to match the extracted minutiae feature points, so as to achieve the purpose of matching fingerprint feature data equally. The simulation results show that the proposed algorithm can effectively reduce the running time and improve the matching accuracy, which fully verifies the practical application value of the algorithm.