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The indoor localization algorithm based on the behavior-driven predictive learning (BDPLA) executes machine-learning predictions by computing the shortest path from a starting location to a destination. The proposed algorithm selects a set of reference points (RPs) to predict the shortest path using all available RPs from the crowdsourced Wi-Fi environment. In addition, the proposed algorithm utilizes the collected received signal strength indicator (RSSI) values to determine the error distance. Using principal component analysis (PCA), the existing crowdsourced RSSI data can be calibrated to help decrease the inconsistent RSSI values among all received signals by reconstructing the values. The average error distance of 3.68 m achieved better results compared with the traditional fingerprint map with an average result of 6.96 m.

Fingerprint recognition systems are susceptible to artificial spoof fingerprint attacks, like molds manufactured from polymer, gelatin or Play-Doh. Presentation attack is an open issue for fingerprint recognition systems. In a presentation attack, synthetic fingerprint which is reproduced from a real user is submitted for authentication. Different sensors are used to capture the live and fake fingerprint images. A liveness detection system has been designed to defeat different classes of spoof attacks by differentiating the features of live and fake fingerprint images. In the past few years, many hardware- and software-based approaches are suggested by researchers. However, the issues still remain challenging in terms of robustness, effectiveness and efficiency. In this paper, we explore all kinds of software-based solution to differentiate between real and fake fingerprints and present a comprehensive survey of efforts in the past to address this problem.

True minutiae extraction in fingerprint image is critical to the performance of an automated identification system. Generally, a set of endings and bifurcations (both called feature points) can be obtained by the thinning image from which the true minutiae of the fingerprint are extracted by using the rules based on the structure of ridges. However, considering some false and true minutiae have similar ridge structures in the thinning image, in a lot of cases, we have to explore their difference in the binary image or the original gray image. In this paper, we first define the different types of feature points and analyze the properties of their ridge structures in both thinning and binary images for the purpose of distinguishing the true and false minutiae. Based on the knowledge of these properties, a fingerprint post-processing approach is developed to eliminate the false minutiae and at the same time improve the thinning image for further application. Many experiments are performed and the results have shown the great effectiveness of the approach.

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.

Most of the fingerprint matching techniques require extraction of minutiae that are ridge endings or bifurcations of ridge lines in a fingerprint image. Crucial to this step is either detecting ridges from the gray-level image or binarizing the image and then extracting the minutiae. In this work, we firstly exploit the property of almost equal width of ridges and valleys for binarization. Computing the width of arbitrary shapes is a nontrivial task. So, we estimate the width using Euclidean distance transform (EDT) and provide a near-linear time algorithm for binarization. Secondly, instead of using thinned binary images for minutiae extraction, we detect minutiae straightaway from the binarized fingerprint images using EDT. We also use EDT values to get rid of spurs and bridges in the fingerprint image. Unlike many other previous methods, our work depends minimally on arbitrary selection of parameters.

Early detection and intervention strategies for schizophrenia are receiving increasingly more attention. Dermatoglyphic patterns, such as the degree of asymmetry of the fingerprints, have been hypothesized to be indirect measures for early abnormal developmental processes that can lead to later psychiatric disorders such as schizophrenia. However, previous results have been inconsistent in trying to establish the association between dermatoglyphics and schizophrenia. The goal of this work is to try to resolve this problem by borrowing well-developed techniques from the field of fingerprint matching. Two dermatoglyphic asymmetry measures are proposed that draw on the orientation field of homologous fingers. To test the capability of these measures, fingerprint images were acquired digitally from 40 schizophrenic patients and 51 normal individuals. Based on these images, no statistically significant association between conventional dermatoglyphic asymmetry measures and schizophrenia was found. In contrast, the sample means of the proposed measures consistently identified the patient group as having a higher degree of asymmetry than the control group. These results suggest that the proposed measures are promising for detecting the dermatoglyphic patterns that can differentiate the patient and control groups.

Traditional fingerprint verifications use single image for matching. However, the verification accuracy cannot meet the need of some application domains. In this paper, we propose to use videos for fingerprint verification. To take full use of the information contained in fingerprint videos, we present a novel method to use the dynamic as well as the static information in fingerprint videos. After preprocessing and aligning processes, the Inclusion Ratio of two matching fingerprint videos is calculated and used to represent the similarity between these two videos. Experimental results show that video-based method can access better accuracy than the method based on single fingerprint.

With the rapid development of smart devices and WiFi networks, WiFi-based indoor localization is becoming increasingly important in location-based services. Among various localization techniques, the fingerprint-based method has attracted much interest due to its high accuracy and low equipment requirement. Traditional fingerprint-based indoor localization systems mostly obtain positioning by measuring the received signal strength indicator (RSSI). However, the RSSI is affected by environmental influences, thereby limiting the precision of positioning. Therefore, we propose a new indoor fingerprint localization system based on channel state information (CSI). We adopt a novel method, in which the amplitude and phase of the CSI are fused to generate fingerprints in the training phase and apply a weighted $k$-nearest neighbor (KNN) algorithm for fingerprint matching during the estimation phase. The system is validated in an exhibition hall and laboratory and we also compare the results of the proposed system with those of two CSI-based and an RSSI-based fingerprint localization systems. The results show that the proposed system achieves a minimum mean distance error of 0.85m in the exhibition hall and 1.28m in the laboratory, outperforming the other systems.

In the concrete implementation of the fuzzy vault algorithm, the geometric hash method is a common technique for automatic calibration of biometric templates. For the fuzzy problem of parameter acquisition, the matching accuracy of fuzzy vault template is affected in the three parameters: the pixel size, hash table and hash table quantization parameters ($\alpha$ and $\beta$). The single factor experiment method obtains the optimal range of these three parameters, and the extraction range of the fuzzy point and the selection rule of the base point distance are improved for the fuzzy vault algorithm. Finally, based on the FVC fingerprint database, their matching precision is compared for the algorithm before and after optimization. The experimental results show that the false rejection rate (FRR) of the optimized algorithm is reduced by at least 9.84%, and the false acceptance rate (FAR) is reduced by at least 7.12%, indicating that the optimization scheme improves the matching accuracy of the algorithm. The algorithm has certain robustness and practicability.

A generalized memristor consisting of a memristive diode bridge with a first order parallel RC filter is proposed in this letter. The mathematical model of the circuit is established and its fingerprints are analyzed by the pinched hysteresis loops with different periodic stimuli. The results verified by experimental measurements indicate that the proposed circuit is a simple voltage-controlled generalized memristor.

This paper deals with the possibility of using ARTMAP neural network for searching fingerprint patterns from a large database. ARTMAP has the ability to perform concurrent processing, to learn fast, and to make decisions. Since ARTMAP learning is self-stabilizing, it can continue to learn from one or more databases, without performance degradation, until its full memory capacity is utilized. Generally, fingerprint matching is based on local ridge characteristics, and its efficiency depends on minutiae extraction. The proposed method uses only gray level values of the image pixels along with its neighboring ones, instead of ridge features.

In this paper, micrometer SrMoO₄:Eu${}^{3+}$ phosphors with different morphologies were prepared by poly(acrylic acid)-assisted hydrothermal method, and the structure, energy band and luminescence properties of SrMoO₄:Eu${}^{3+}$ were studied in detail. Furthermore, the relationship between the phosphors’ structure and luminescence properties was discussed. The modified SrMoO₄:Eu${}^{3+}$ have great potential as functional materials in fluorescent fingerprint and LED device applications. The results indicated that the poly(acrylic acid) and the hydrothermal condition are the key factors for the achievement of better luminescent performances.

Automatic fingerprint identification methods have become the most widely used technology in rapidly growing bioidentification applications. In this paper, different image enhancement approaches presented in the scientific literature are reviewed. Fingerprint verification can be divided into image acquisition, enhancement, feature extraction and matching steps. The enhancement step is needed to improve image quality prior to feature extraction. By far the most common approach relies on the filtering of the fingerprint images with filters adapted to local ridge orientation, but alternative approaches based on Fourier domain processing, direct ridge following and global features also exist. Methods of comparing the performance of enhancement methods are discussed. An example of the performance of different methods is given. Conclusions are made regarding the importance of effective enhancement, especially for noisy or low quality images.

Issues with verification speed improvement to fingerprint systems are investigated in this paper. First, the impact of verification speed on the overall system performance is highlighted. Then a rather general speed enhancement procedure for algorithms is proposed, consisting of two or more pattern matching stages for refining the value of the discrimination function. Special attention is paid to the algorithm supporting the verification procedure used to determine the two verification thresholds. It is shown, and supported by numerical examples, that significant verification speed improvement can be achieved without sacrificing the system accuracy.

The aim of any fingerprint image compression technique is to achieve a maximum amount of compression with an adequate quality compressed image which is suitable for fingerprint recognition. Currently available techniques in the literature provide 100% recognition only up to a compression ratio of 180:1. The performance of any identification technique inherently depends on the techniques with which images are compressed. To improve the identification accuracy while the images are highly compressed, a multiwavelet-based identification approach is proposed in this paper. Both decimated and undecimated coefficients of SA4 (Symmetric Antisymmetric) multiwavelet are used as features for identification. A study is conducted on the identification performance of the multiwavelet transform with various sizes of images compressed using both wavelets and multiwavelets for fair comparison. It was noted that for images with size power of 2, the decimated multiwavelet-based compression and identification give a better performance compared to other combinations of compression/identification techniques whereas for images with size not a power of 2, the undecimated multiwavelet transform gives a better performance compared to other techniques. A 100% identification accuracy was achieved for the images from NIST-4, NITGEN, FVC2002DB3_B, FVC2004DB2_B and FVC2004DB1_B databases for compression ratios up to 520:1, 210:1, 445:1, 545:1 and 1995:1, respectively.

Over the past decade, there have been dramatic increases in the usage of mobile phones in the world. Currently available smart mobile phones are capable of storing enormous amounts of personal information/data. The smart mobile phone is also capable of connecting to other devices, with the help of different applications. Consequently, with these connections comes the requirement of security to protect personal information. Nowadays, in many applications, a biometric fingerprint recognition system has been embedded as a primary security measure. To enable a biometric fingerprint recognition system in smart mobile phones, without any additional costs, a built-in high performance camera can be utilized. The camera can capture the fingerprint image and generate biometric traits that qualify the biometric fingerprint authentication approach. However, the images acquired by a mobile phone are entirely different from the images obtained by dedicated fingerprint sensors. In this paper, we present the current trend in biometric fingerprint authentication techniques using mobile phones and explore some of the future possibilities in this field.

Fingerprint verification systems have attracted much attention in secure organizations; however, conventional methods still suffer from unconvincing recognition rate for noisy fingerprint images. To design a robust verification system, in this paper, wavelet and contourlet transforms (CTS) were suggested as efficient feature extraction techniques to elicit a coverall set of descriptive features to characterize fingerprint images. Contourlet coefficients capture the smooth contours of fingerprints while wavelet coefficients reveal its rough details. Due to the high dimensionality of the elicited features, across group variance (AGV), greedy overall relevancy (GOR) and Davis–Bouldin fast feature reduction (DB-FFR) methods were adopted to remove the redundant features. These features were applied to three different classifiers including Boosting Direct Linear Discriminant Analysis (BDLDA), Support Vector Machine (SVM) and Modified Nearest Neighbor (MNN). The proposed method along with state-of-the-art methods were evaluated, over the FVC2004 dataset, in terms of genuine acceptance rate (GAR), false acceptance rate (FAR) and equal error rate (EER). The features selected by AGV were the most significant ones and provided 95.12% GAR. Applying the selected features, by the GOR method, to the modified nearest neighbor, resulted in average EER of $<1$%, which outperformed the compared methods. The comparative results imply the statistical superiority ($p<0.05$) of the proposed approach compared to the counterparts.

Fingerprint matching is a key technique of automatic fingerprint recognition system. The result of point pattern matching is accurate while its repeated search for maximum likelihood pairs are very time-consuming. In this paper, we proposed a fast complex fingerprint matching method to solve this problem. The innovative method constructs rotation-and-translation-invariant feature vectors to describe local information of minutiae sets and shifts the matching style from point pattern to feature vector. If the decision is uncertain by this case, then it estimates rotation and translation parameters from previous matched pairs and returns to point matching. Since this method takes the similarity and consistency of local deformation into account, it is not only much faster, but also lower false accepted than traditional approaches. The test on FVC2004 data sets shows that the equal error rate (EER) is 2.97%, and the mean matching time is 10.3 milliseconds, variance is 2.03, superior to most traditional methods. When minutiae number is among 15-40, the EER reaches to 2.14%.

Frequency information is very important in the enhancement of the fingerprint image while it is very difficult to be estimated accurately in some areas. In this paper, a new and robust frequency estimation algorithm is proposed which is completely independent of the local orientation information and ridge structure. As a narrowband texture image, there are different output responses through different scale Gabor filters and the maximum response will correspond to the right frequency. The experimental results show our algorithm is accurate and it provides a novel approach of fingerprint frequency estimation.

Fingerprint enhancement determines the performance of automatic fingerprint recognition system, and it is necessary to repeatedly enhance the ridges due to the quality variety in a fingerprint image. This paper presents a novel adaptive enhancement algorithm by estimation of quality in local ridges. The algorithm can automatically adjust the parameters of filters and the time of filtering according to the quality factors in different regions. In order to improve filtering efficiency, a template bank of 4-dimenstion array is also designed to quantize the filter. Experimental results in eight low-quality images from FVC2004 data sets show that the proposed algorithm is higher 0.11 (or 23.7%) in Good Index (GI), and less 0.36625 second (or 54.06% time savings) in time consumptions than traditional Gabor-based methods. Since the eight images are extremely bad, a little improvement will be very meaningful. Furthermore, the variance of time consumption shows that the traditional method heavily relies on the quality of input fingerprint image and exposes the disadvantage of indistinctively repeated filtering. So the proposed method can avoid the defect of repeated filtering, protect minutiae, and improve clarity of ridges.