Efficient Fingerprint Research Articles

Data Deduplication System Based on Content-Defined Chunking Using Bytes Pair Frequency Occurrence

Every second, millions of data are being generated due to the use of emerging technologies. It is very challenging to store and handle such a large amount of data. Data deduplication is a solution for this problem. It is a new technique that eliminates duplicate data and stores only a single copy of data, reducing storage utilization and the cost of maintaining redundant data. Content-defined chunking (CDC) has been playing an important role in data deduplication systems due to its ability to detect high redundancy. In this paper, we focused on deduplication system optimization by tuning relevant factors in CDC to identify chunk cut-points and introduce an efficient fingerprint using a new hash function. We proposed a novel bytes frequency-based chunking (BFBC) algorithm and a new low-cost hashing function. To evaluate the efficiency of the proposed system, extensive experiments were done using two different datasets. In all experiments, the proposed system persistently outperformed the common CDC algorithms, achieving a better storage gain ratio and enhancing both chunking and hashing throughput. Practically, our experiments show that BFBC is 10 times faster than basic sliding window (BSW) and approximately three times faster than two thresholds two divisors (TTTD). The proposed triple hash function algorithm is five times faster than SHA1 and MD5 and achieves a better deduplication elimination ratio (DER) than other CDC algorithms. The symmetry of our work is based on the balance between the proposed system performance parameters and its reflection on the system efficiency compared to other deduplication systems.

Fast algorithms for single and multiple pattern Cartesian tree matching

Cartesian tree matching is the problem of finding every substring of a given text which has the same Cartesian tree as that of a given pattern. In this paper we propose fast algorithms for single and multiple pattern Cartesian tree matching by introducing new representations and encodings. For single pattern Cartesian tree matching, we present the framework of a binary filtration method and an efficient verification technique. Any exact string matching algorithm can be used as a filtration for Cartesian tree matching in our framework. For multiple pattern Cartesian tree matching, we present two fingerprinting methods, i.e., the parent-distance encoding and the binary encoding. By combining an efficient fingerprinting method and a conventional multiple string matching algorithm, we can efficiently solve multiple pattern Cartesian tree matching. By experiments we show that our matching algorithms provide good performances for both single and multiple pattern Cartesian tree matching.

Bio-AKA: An efficient fingerprint based two factor user authentication and key agreement scheme

The fingerprint has long been used as one of the most important biological features in the field of biometrics. It is person-specific and remain identical though out one’s lifetime. Physically uncloneable functions (PUFs) have been used in authentication protocols due to the unique physical feature of it. In this paper, we take full advantage of the inherent security features of user’s fingerprint biometrics and PUFs to design a new user authentication and key agreement scheme, namely Bio-AKA, which meets the desired security characteristics. To protect the privacy and strengthen the security of biometric data and to improve the robustness of the proposed scheme, the fuzzy extractor is employed. The scheme proposed in the paper can protect user’s anonymity without the use of password and allow mutual authentication with key agreement. The experimental results show superior robustness and the simplicity of our proposed scheme has been validated via our performance and security analysis. The scheme can be an ideal candidate for real life applications that requires remote user authentication.

IMCI: an efficient fingerprint retrieval approach based on 3D stacked memory

IMCI: an efficient fingerprint retrieval approach based on 3D stacked memory

An Efficient Fingerprint Database Construction Approach Based on Matrix Completion for Indoor Localization

The Received Signal Strength (RSS) fingerprint-based indoor localization method in Wireless Local Area Network (WLAN) environment has been widely used due to its low hardware requirements and high positioning accuracy. However, the positioning system requires enormous effort to collect fingerprint at each reference point in positioning area, which is time-consuming and laborious. For the practicability of the method, it is essential to reduce the workload of fingerprint collection in the offline phase and construct a fingerprint database efficiently and accurately. Utilizing the high spatial correlation of RSS, in this paper, an efficient fingerprint database construction method is proposed based on low-rank matrix completion. In the meanwhile, different from the traditional matrix completion model, we propose an improved optimization model by converting to the weighted nuclear norm and introducing l 1 -norm and Frobenius norm to recover unknown data accurately and reduce the influence of outliers and Gaussian noise simultaneously. Furthermore, we combine the K-Nearest Neighbor (KNN) method to solve the problem that matrix completion cannot deal with all unknown rows or columns in the matrix. The experiment results indicate that the proposed algorithm can recover the fingerprint database completely through a few RSS information and has higher accuracy than other algorithms in various scenarios.

Fingerprint Identification With Shallow Multifeature View Classifier.

This article presents an efficient fingerprint identification system that implements an initial classification for search-space reduction followed by minutiae neighbor-based feature encoding and matching. The current state-of-the-art fingerprint classification methods use a deep convolutional neural network (DCNN) to assign confidence for the classification prediction, and based on this prediction, the input fingerprint is matched with only the subset of the database that belongs to the predicted class. It can be observed for the DCNNs that as the architectures deepen, the farthest layers of the network learn more abstract information from the input images that result in higher prediction accuracies. However, the downside is that the DCNNs are data hungry and require lots of annotated (labeled) data to learn generalized network parameters for deeper layers. In this article, a shallow multifeature view CNN (SMV-CNN) fingerprint classifier is proposed that extracts: 1) fine-grained features from the input image and 2) abstract features from explicitly derived representations obtained from the input image. The multifeature views are fed to a fully connected neural network (NN) to compute a global classification prediction. The classification results show that the SMV-CNN demonstrated an improvement of 2.8% when compared to baseline CNN consisting of a single grayscale view on an open-source database. Moreover, in comparison with the state-of-the-art residual network (ResNet-50) image classification model, the proposed method performs comparably while being less complex and more efficient during training. The result of classification-based fingerprint identification has shown that the search space is reduced by over 50% without degradation of identification accuracies.

Novel similarity measure-based random forest for fingerprint recognition using dual-tree complex wavelet transform and ring projection

Designing an efficient fingerprint recognition technique is an ill-posed problem. Recently, many researchers have utilized machine learning techniques to improve the fingerprint recognition rate. The random forest (RF) is found to be one of the extensively utilized machine learning techniques for fingerprint recognition. Although it provides good recognition results at significant computational speed, still there is room for improvement. RF is not so-effective for high-dimensional features and also when features contain both discrete and continuous values at the same time. Therefore, in this paper, a novel similarity measure-based random forest (NRF) is proposed. The proposed technique, initially, computes both mutual information and conditional entropy. Thereafter, it uses three designed if-then rules to obtain final information measure. Additionally, to obtain feature set for fingerprint dataset, dual-tree complex wavelet transform is used to evaluate complex detail coefficients. Thereafter, ring project is considered to compute significant moments from these complex detail coefficients. Finally, information gain-based feature selection technique is used to select potential features. To prevent over-fitting, 20-fold cross validation is also used. Extensive experiments are considered to evaluate the effectiveness of the proposed technique. The comparative analyses reveal that the proposed technique outperforms the existing techniques in terms of accuracy, f-measure, sensitivity, specificity, kappa statistics and computational speed.

Fingerprint recognition system based on modified multi-connect architecture (MMCA)

Fingerprinting is the most widely used and recognised biometric technology for human authentication. Fingerprint authentication has a proven record as highly secure and convenient as compared to passwords. Hence, fingerprint sensing has come to berecognized as a common and product-differentiating feature in smartphones, tablets and PCs. This paper proposes to develop a Fingerprint recognition system for authentication of personsby using a new technique, termed as ‘associative memory with modify multi-connect architecture’. This, in turn, may pave the way to develop more efficient Fingerprint systemshaving accuracy and lesserprocessing time. Further, with application of additional tranches of associative memory, such systems in the future will acquirepotential to perform highly complex operations and save memory. In this paper, three databases viz., FVC (2004) database, internal database and International NIST database 4 are used. FVC (2004) database contains 640 fingerprint patterns, while internal database contains 2500 different fingerprint patterns; and the International NIST database 4 consists of 2000 pairs of fingerprint patterns. The proposed fingerprint recognition system has an average accuracy of 99.56% and a pattern recognition processing time of approximately 30 s.

An Efficient Fingerprint Matching by Multiple Reference Points

An Efficient Fingerprint Matching by Multiple Reference Points

Multi-Phase Fingerprint Map Based on Interpolation for Indoor Localization Using iBeacons

It has traditionally been laborious and problematic to construct and maintain fingerprint maps for indoor positioning. In this paper, a multi-phase fingerprint map indoor localization method based on interpolation was proposed. The method addresses two needs: (1) to construct an efficient fingerprint map, and (2) to provide high-accuracy indoor positioning. For the effective collection of the real-time received signal strength indicator (RSSI), we proposed a mobile data-collection cart, equipped with a laser rangefinder and a smartphone, which can rapidly collect environmental data in indoor spaces. In practice, as some regions might be inaccessible, we adopted a kriging-based interpolation method that exploits the spatial autocorrelation of the RSSI to efficiently generate and update the fingerprint database. To overcome the instability of RSSI and improve positioning accuracy, we proposed a time-variant multi-phase fingerprint map, with specific fingerprint databases constructed for different time periods, thereby automatically employing the most appropriate fingerprint map according to the time period. Indoor experiments verified that the proposed method has practical value, with sufficient accuracy for general indoor applications. We believe that the proposed method provides a feasible and low-cost indoor positioning solution.

Efficient fingerprint matching on smart cards for high security and privacy in smart systems

Smart control access to any service is at the very basis of any smart project. Biometrics have been used as a solution for service access control an any smart system, for many years now. However, the simple use of biometrics cannot be considered as final and perfect solution. Most problems are related to the data transmission method between the medias, for which the users require access and the servers, wherein the biometric data, captured upon registration, are stored. In this paper, we use smart cards as an effective yet efficient solution to this critical data storage problem. Furthermore, fingerprints have been used as a human identifier for some time now. This biometric is considered one of the most reliable to distinguish a person from another as its unique yet perfectly stable over time. In this work, we propose an efficient implementation of fingerprint verification on smart cards. For this implementation, the matching is done on-card. Thus, the biometric characteristics are always kept in the owner’s card, guaranteeing the maximum security and privacy. The proposed implementation is based on the Skin Elasticity Tolerant Algorithm (SETA), which uses minutiae to implement fingerprints matching. It consists of finding the best alignment between the compared fingerprints before proceeding with the comparison step to maximize the score that quantifies the quality of the matching. Although efficient, SETA is not suitable for card implementation because it requires a lot of dynamic storage memory to store the counters of all possible rotations and translations. In order to make the implementation on smart cards viable mitigating the problem of the volume of storage required to store the translations and rotations, we subdivide the search space into small subspaces. Thus, only the translations and rotations that fall within to the handled subspace at each instant are considered. The implementation on smart cards is evaluated for known fingerprint datasets. Several settings are tested, establishing that the implementation is suitable for real-time applications.

Enhancement of fingerprint image using wiener filter

A fingerprint is one of the most vital Biometric traits used for Personal Identification. To identify and match the fingerprint accurately, it has to be enhanced efficiently. In this paper, an efficient fingerprint enhancement technique is adopted and compared with the existing methods. The proposed methodology consists of three Phases. In the first phase, the fingerprint is subjected to the de-noising process. After adding noise such as salt and pepper, Gaussian and speckle noise, the image is blurred. In the second phase, the fingerprint is filtered with Wiener filter and then de-blurred. In the third, the filtered image is further enhanced for more clarity. The paper emphasizes, the fingerprint preprocessing followed with the enhancement produces better quality image. The performance of the proposed methodology is compared and evaluated using two performances measures namely Peak-Signal-Noise –Ratio and Mean Squared Error using Matlab R2013a.

An efficient fingerprint identification algorithm based on minutiae and invariant moment

While we are experiencing many advantages of digital technologies and products, the security issues are also attracting increasing concerns. The secure fingerprint identification has become one of ...

ISR: indoor shop recognition via user-friendly and efficient fingerprinting on smartphones

Indoor shop recognition can not only help mobile users to quickly recognize a shop to know about the information of interest without needing to enter the shop, but also assist in achieving more accurate user localization in a shopping mall. However, the existing Wi-Fi fingerprint-based approaches or image-based approaches cannot accomplish this goal well due to a huge cost of constructing large-scale fingerprint database and poor accuracy. In order to address these issues, we proposed a user-friendly and efficient fingerprinting method to collect various valuable sensory data with smartphones, which can not only reduce the randomness of fingerprints and the negative impact of pedestrians in image matching, but also be used to derive the user-to-shop distance based on the perspective projection model for assisting in determining an accurate fingerprint searching scope. We also proposed an efficient fingerprint searching and matching method to improve the recognition accuracy. We implemented a prototype system and collected fingerprint datasets in a shopping mall. Extensive experiments demonstrate that our solution achieves promising results in realistic scenarios.

Measurement and validation of uranium isotope ratio in uranium ore for isotopic fingerprinting

Accurate determination of uranium isotope ratio can act as an efficient fingerprint in the nuclear forensics to identify source and intended use of illicit trafficking of uranium material. In this context, a rapid chemical separation technique to isolate uranium from uranium ore sample was developed using commercial extraction chromatographic resin. Uranium isotope ratio was measured using thermal ionization mass spectrometry (TIMS). Standard reference material (NBS U010) was used for validating the accuracy and precision of isotope ratio measurement by TIMS. The method is successfully applied to a natural ore (uraninite ore) for the determination of naturally occurring uranium isotope ratio.

A robust and low-cost video fingerprint extraction method for copy detection

Video fingerprinting for content-based video identification is a very useful task for the management and monetization of copyrighted content distribution. The main challenges of monitoring and copy detection systems are: a) the effective identification of highly transformed videos (robustness) and b) computational efficiency which may be relevant for some applications. Typically, most video fingerprinting methods focus on robustness leaving aside computational efficiency. However, for real-time applications are necessary low computational cost detection methods, for instance, in illegal content monitoring in video streaming distributions. Therefore, in this paper, we propose a low-cost and effective video fingerprint extraction method based on the combination of content-based features using both acoustic and visual video components. Our method is capable of detecting video copies by using computationally efficient fingerprints while maintaining robustness against the decrease in quality and content preserved distortions, which are frequent but severe attacks.

Generation of efficient fingerprint for GFP-like fold and computational identification of potential GFP-like homologs

There is a considerable interest in the detection of GFP-like proteins due to their structural stability and functional usefulness. GFP-like proteins share highly conserved beta-barrel fold with 11 beta-strands. However, their low sequence identity hampers efficient identification of their homologous proteins from database. In this study, an attempt was made to generate a fingerprint for efficient detection of GFP-like proteins. Overlapped conserved residues (OCR)-based approach has been used to design a protein fingerprint based on sequentially and structurally conserved residues in secondary structures to detect homologous proteins very efficiently. Therefore, a fingerprint for GFP-like fold was designed using the OCR approach. However, its specificity was too low to be used for the identification of novel proteins. The conserved residues of loop regions were added and optimized to improve its specificity without losing its high sensitivity. The optimized fingerprint was employed to scan NR database. A total of 20 hypothetical proteins were detected, among which nine were validated as potential GFP-like homologs.

Chemical Fingerprint Analysis for the Quality Evaluation of Deepure Instant Pu-erh Tea by HPLC Combined with Chemometrics

A simple and efficient fingerprint analysis method was developed and validated for the quality consistency control and authentication of Deepure instant pu-erh tea within different manufacturers using high-performance liquid chromatography (HPLC) coupled with chemometrics. To compare the HPLC fingerprint chromatograms between batches, varieties and manufacturers, the correlation coefficients of similarities of 60 samples and the relative peak areas of 11 characteristic compounds were calculated. Different pattern recognition procedures, including hierarchical cluster analysis, principal component analysis and partial least squares discriminant analysis, were then applied to classify the instant pu-erh tea samples according to their variety and manufacturer. Results showed that the developed method has potential application for the quality consistency evaluation and discrimination of instant pu-erh tea samples from similar products.

Tracking the signals of living archaea: A multiple reaction monitoring (MRM) method for detection of trace amounts of intact polar lipids from the natural environment

Isoprenoid glycerol dialkyl glycerol tetraethers (iGDGTs) are biomarkers of archaea, but improvement is needed for more precise analysis of intact polar lipid (IPL)-iGDGTs. Here, we present a multiple reaction monitoring (MRM) method for IPL-iGDGTs using reversed phase liquid chromatography (RP-LC) and triple quadrupole mass spectrometry equipped with electrospray ionization (RP-LC–ESI-MRM). The MRM mode showed much greater sensitivity and lower detection limits than the commonly used selected ion monitoring (SIM) mode. Furthermore, the MRM mode had better stability and repeatability than the SIM mode. Thus, the RP-LC–ESI-MRM method can allow efficient fingerprinting of archaeal IPL-iGDGTs, particularly when they are in trace abundance in natural environments.

Development of Chemical Fingerprints for Quality Control of Xiong Ma Tang and its Related Preparations by High-Performance Liquid Chromatography

Xiong Ma Tang (XMT), one of the ancient herbal prescriptions, has been used in treatment of migraine disease. In this study, efficient high-performance liquid chromatographic fingerprints were developed for identification of 11 major marker components, named 1-(β-D-ribofuranosyl)-1H-1,2,4-triazone, gastrodin, ligustrazine, 3,4-bihydroxy benzaldehyde, p-hydroxy benzaldehyde, p-hydroxybenzoic acid, benzoic acid, vanillin, ferulic acid, caffeic acid and senkyunolide I, respectively, in XMT and three related preparations. The analysis of 11 marker compounds was carried out using a Kromasil C18 reserved-phase column (5 μm, 4.6 × 250 mm) and gradient elution with methanol and aqueous phosphoric acid (0.1%, v/v). The flow rate was 1.0 mL/min and the detector wavelength was set at 276 nm. Mean chromatograms and correlation coefficients of samples were calculated by the software "Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine". The correlation coefficients of XMT and three related preparations ranged from 0.935 to 0.986, 0.909 to 0.949, 0.900 to 0.926 and 0.937 to 0.988, respectively. It was the first time 11 peaks of XMT and three related preparations were identified by comparing with standard compounds. These results have shown that the developed method was efficient for the quality evaluation of XMT and its related preparations.