Query Fingerprint Research Articles

Deep Feed-Forward Neural Network-Based Biometric Authentication System with Biometric Identity and Reputation Score in Blockchain

The biometric authentication in today’s digital era has become a vital issue in security and privacy. Biometrics is currently becoming more significant specifically for higher security modules. Here, a deep feed-forward neural network-based biometric authentication system (DFFNN_biometric authentication system) is presented for biometric authentication utilizing a biometric fingerprint image. In blockchain network, biometric data are considered and the fingerprint images are fed as input. As an input fingerprint image, minutiae extraction is accomplished. Thereafter, a deep key is generated employing a deep residual network (DRN) for template protection. Thereafter, privacy protection template generation is conducted and the templates thus obtained are stored on a database (DB). On the other hand, a query fingerprint image is given as input in the authentication stage. The processes such as minutiae extraction, deep key generation using DRN and privacy protection template authentication are carried out as like it performed in the blockchain network. Additionally, the matching process is conducted between the output acquired in the authentication phase and image stored in DB. The reputation score is generated from authenticated biometric behavior. The DFFNN is utilized to generate a reputation score. Moreover, the DFFNN_biometric authentication system obtained a maximum accuracy of 90.8%, maximum genuine accept rate (GAR) of 0.936, minimum false acceptance rate (FAR) of 0.588, minimum false rejection rate (FRR) of 0.556 and maximum reputation score of 9.346.

Multi-Instance Contingent Fusion for the Verification of Infant Fingerprints

It is imperative to establish an automated system for the identification of neonates (1–28 days old) and infants (29 days–12 months old) through the utilisation of the readily accessible 500 ppi fingerprint reader. This measure is crucial in addressing the issue of newborn swapping, facilitating the identification of missing children, monitoring immunisation records, maintaining comprehensive medical history, and other related purposes. The objective of this study is to demonstrate the potential for future identification of infants using fingerprints obtained from a 500 ppi fingerprint reader by employing a fusion technique that combines multiple instances of fingerprints, specifically the left thumb and right index fingers. The fingerprints were acquired from babies who were between the ages of one day and six months at the enrolment session. The sum-score fusion algorithm was implemented. The approach mentioned above yielded verification accuracies of 73.8%, 69.05%, and 57.14% for time intervals of 1 month, 3 months, and 6 months, respectively, between the enrolment and query fingerprints.

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 “query2set” 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.

Automatic latent fingerprint identification system using scale and rotation invariant minutiae features

In this paper, we propose a new clustered minutiae-based scale and rotation invariant fingerprint matching method. The major challenge faced in the existing latent fingerprint identification system is the lack of minutiae features in the fingerprint regions and hence there is a requirement to utilize the existing minutiae arrangements in the regions to identify the query fingerprint. We have clustered minutiae around a reference minutia and generated minutiae invariants to identify the fingerprint. In this paper, we propose two algorithms based on the minutiae neighborhood. To solve the geometrical constraints between the pairs of nearest points around a minutia, we propose the latent minutiae similarity (LMS) algorithm. Based on geometrical arrangements on the set of latent minutiae patterns around a minutia, we propose a clustered latent minutiae pattern (CLMP) algorithm. We test our algorithms on the FVC2004 and NIST SD27 criminal fingerprint databases. Proposed LMS, CLMP algorithms produced the highest 97.5% and 100% of Rank-1 identification accuracy respectively on plain FVC2004 dataset. Whereas, for NIST SD27 latent fingerprint database the proposed LMS, CLMP algorithms produced the highest Rank-1 identification accuracy of 88.8% and 93.80% respectively. Experimental results show significant improvement in the Rank-1 matching accuracy under random fingerprint scale and rotation condition compared to the state-of-the-art algorithms.

Fingerprint pore matching using deep features

As a popular living fingerprint feature, sweat pore has been adopted to build robust high resolution automated fingerprint recognition systems (AFRSs). Pore matching is an important step in high resolution fingerprint recognition. This paper proposes a novel pore matching method with high recognition accuracy. The method mainly solves the pore representation problem in the state-of-the-art direct pore matching method. By making full use of the diversity and large quantities of sweat pores on fingerprints, deep convolutional networks are carefully designed to learn a deep feature (denoted as DeepPoreID) for each pore. The inter-class difference and intra-class similarity of pore patch pairs can be well solved using deep learning. The DeepPoreID is then used to describe the local feature for each pore and finally integrated into the classical direct pore matching method. More specifically, pore patches, which are cropped from both Query and Template fingerprint images, are imported into the well-trained networks to generate DeepPoreID for pore representation. The similarity between those DeepPoreIDs are then obtained by calculating the Euclidian Distance between them. Subsequently, one-to-many coarse pore correspondences are established via comparing their similarity. Finally, classical Weighted RANdom SAmple Consensus (WRANSAC) is employed to pick true pore correspondences from coarse ones. The experiments carried on the two public high resolution fingerprint database have shown the effectiveness of the proposed DeepPoreID, especially for fingerprint matching with small image size. Meanwhile, better recognition accuracy is achieved by the proposed method when compared with the existing state-of-the-art methods. About 35% rise in equal error rate (EER) and about 30% rise in FMR1000 when compared with the best result evaluated on the database with image size of 320 × 240 pixels.

Multimodal Image Fusion in Biometric Authentication

During this study, a unique multimodal biometric system was constructed. This system incorporated a variety of unimodal biometric inputs, including fingerprints, palmprints, knuckle prints, and retina images. The multimodal system generated the fused template via feature-level fusion, which combined several different biometric characteristics. The Gabor filter extracted the features from the various biometric aspects. The fusion of the extracted information from the fingerprint, knuckle print, palmprint, and retina into a single template, which was then saved in the database for authentication, resulted in a reduction in both the spatial and temporal complexity of the process. A novel technique for safeguarding fingerprint privacy has been developed to contribute to the study. This system integrates the unique fingerprints of the thumb, index finger, and middle finger into a single new template. It was suggested that the Fixed-Size Template (FEFST) technique may be used might develop a novel strategy for the extraction of fingerprint features. From each of the fingerprints, the minute locations of the ridge end and ridge bifurcations as well as their orientations relative to the reference points were retrieved. The primary template was derived from the fingerprint that included the greatest number of ridge ends. For the purpose of generating the combined minutiae template, the templates of the other two fingerprints were incorporated into this template. The merged minutiae template that was developed was then saved in a database so that registration could take place. During the authentication process, the system received the three query fingerprints, and those fingerprints were compared to the previously saved template.

Fingerprint indexing schemes – A survey

Biometric authentication involves identification/verification of an individual based on his physiological or behavioral characteristics. Fingerprint, a physiological characteristic, is an impression developed on a surface touched by a human fingertip. As the use of fingerprint recognition grows, more and more people get registered to the system and therefore the size of biometric database increases. During identification when a new fingerprint is provided and the task is to find the most similar fingerprint from the database, it essentially involves searching the entire database. Identification becomes very compute intensive for large databases. If we can somehow reduce the search space from the entire database to a small size list, the identification would become cost-effective. One possibility is to use locality sensitive hashing based approaches. The key idea is to choose an appropriate representation of the fingerprint and to device an indexing approach around it. Such a technique would, instead of directly comparing the query fingerprint with all the fingerprints stored in the database, make a preprocessing to produce a small size candidate list of fingerprints and then search within the list. The candidate list contains the target fingerprint with certain probabilistic guarantees. Indexing is challenging for biometric databases due to various factors such as the presence of the variable number of features, absence of structural information like order, the presence of occlusion and illumination in the image etc. This paper presents a survey of indexing schemes used for fingerprint databases. These schemes have been classified into four broad categories, namely texture-based, minutiae-based, hybrid and deep learning based schemes. The schemes broadly cover the use of Level-1, Level-2, texture, and deep features.

Bayesian networks for mass spectrometric metabolite identification via molecular fingerprints.

MotivationMetabolites, small molecules that are involved in cellular reactions, provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem mass spectrometry to identify the thousands of compounds in a biological sample. Recently, we presented CSI:FingerID for searching in molecular structure databases using tandem mass spectrometry data. CSI:FingerID predicts a molecular fingerprint that encodes the structure of the query compound, then uses this to search a molecular structure database such as PubChem. Scoring of the predicted query fingerprint and deterministic target fingerprints is carried out assuming independence between the molecular properties constituting the fingerprint.ResultsWe present a scoring that takes into account dependencies between molecular properties. As before, we predict posterior probabilities of molecular properties using machine learning. Dependencies between molecular properties are modeled as a Bayesian tree network; the tree structure is estimated on the fly from the instance data. For each edge, we also estimate the expected covariance between the two random variables. For fixed marginal probabilities, we then estimate conditional probabilities using the known covariance. Now, the corrected posterior probability of each candidate can be computed, and candidates are ranked by this score. Modeling dependencies improves identification rates of CSI:FingerID by 2.85 percentage points.Availability and implementationThe new scoring Bayesian (fixed tree) is integrated into SIRIUS 4.0 (https://bio.informatik.uni-jena.de/software/sirius/).

COMBINATION OF FINGERPRINT FOR PRIVACY PROTECTION

Biometric refers to the authentication technique which depends on the measurement and statistical analysis of people's physical and behavioral characteristics. Among all the biometric technique fingerprint identification is consider as most reliable technique as every individual has unique fingerprint. But as fingerprint technique is used in many authentication systems so protecting it becomes an important issue. Here a system is shown to protect the privacy of fingerprint by combining two different fingerprints into a new virtual identity. In enrollment phase, extract the minutiae position from one fingerprint, orientation from another fingerprint and reference points from both fingerprints, using this extracted information from a combined minutiae template and store it in database. In authentication phase use two query fingerprints, extract the minutiae position from one fingerprint, orientation from another fingerprint and reference point from both the query fingerprint. Perform a two stage fingerprint matching process to match the two query fingerprints with the enrolled fingerprint. With the existing fingerprint reconstruction technique a new virtual identity is created.

A Cancelable Fuzzy Vault Algorithm Based on Transformed Fingerprint Features

Juels and Sudan proposed in 2002 an algorithm for computing a fuzzy vault that binds a user's biometric template with his secret. It was suggested that this vault could be used to securely store one's secret or a cryptographic key without losing his biometric information. However, in this classical fuzzy vault, if an attacker captures multiple vaults generated from one biometric template, he is able to obtain some biometric template information by cross-matching, and then he can use it to illegally recover the secret message. To overcome this disadvantage, in this paper, a cancelable fuzzy vault algorithm is proposed based on the user's transformed fingerprint features which are used to generate a fuzzy vault. Our novel fuzzy vault is secure and can overcome the cross-matching attack without intensive computational complexity. Also, the use of three check values makes our vault have a much higher probability to detect a false query fingerprint template than some other vault versions, and it will highly improve the probability whether the reconstructed polynomial is correct or not.

Encrypted domain matching of fingerprint minutia cylinder-code (MCC) with l1 minimization

Fingerprint is a highly discriminative biometric modality and has gained a lot of interest in both forensic and civil applications. However, it is well known that fingerprint template stored in plaintext is vulnerable to attack. A secure fingerprint template has to be both non-invertible and non-linkable. Minutia cylinder-code (MCC) is a fixed-length and highly discriminative type of minutia descriptor. However, most of the current MCC based fingerprint matching algorithms do not possess the non-linkable property. In this paper, a secured fingerprint MCC matching scheme is proposed by utilizing l1-minimization, where the enrolled fingerprint MCC template is stored in cyphertext form (i.e., E-MCC) and is recognizable only when a close enough query fingerprint presented to the system. Our experimental results on FVC2002 DB1, FVC2002 DB2 and FVC2004 DB1 databases show that the proposed system is highly secure and accurate. The GAR with FAR=0 on FVC2002 DB1, FVC2002 DB2 and FVC2004 DB1 are 91.4%, 84.0% and 65.6%, respectively, with a security level of 33 bits. The performance of the proposed encrypted domain matching algorithm outperforms state-of-the-art fingerprint encryption algorithms.

Magnetic Resonance Fingerprinting with compressed sensing and distance metric learning

Magnetic Resonance Fingerprinting (MRF) is a novel technique that simultaneously estimates multiple tissue-related parameters, such as the longitudinal relaxation time T1, the transverse relaxation time T2, off resonance frequency B0 and proton density, from a scanned object in just tens of seconds. However, the MRF method suffers from aliasing artifacts because it significantly undersamples the k-space data. In this work, we propose a compressed sensing (CS) framework for simultaneously estimating multiple tissue-related parameters based on the MRF method. It is more robust to low sampling ratio and is therefore more efficient in estimating MR parameters for all voxels of an object. Furthermore, the MRF method requires identifying the nearest atoms of the query fingerprints from the MR-signal-evolution dictionary with the L2 distance. However, we observed that the L2 distance is not always a proper metric to measure the similarities between MR Fingerprints. Adaptively learning a distance metric from the undersampled training data can significantly improve the matching accuracy of the query fingerprints. Numerical results on extensive simulated cases show that our method substantially outperforms state-of-the-art methods in terms of accuracy of parameter estimation.

English

In this paper we have proposed an innovative fingerprint recognition system by combining two different fingerprints into a new identity which contains minutiae position and minutiae orientation. In the enrollment phase, two fingerprints are obtained from two different fingers and minutiae positions from one fingerprint, the orientation from the other fingerprint. Based on this extracted information, a combined template is generated and stored in database. In the verification phase, the system requires two query fingerprints from the fingers which are used in the enrollment. The fingerprint matching process is done by minutiae-based fingerprint matching algorithms. By storing the combined template, the complete minutiae feature of a single fingerprint will not be compromised when the database is stolen.

Identity of User Thrashing and Privacy Protection of Fingerprints

A novel system used for fingerprint privacy protection by creating a new fingerprint identity and thrashes the user identity within the fingerprint image. A combined minutiae template containing only a partial minutiae feature of each of the two fingerprints captured in the enrolment phase, will be generated and stored. In the authentication process, a two-stage fingerprint matching process is proposed for matching the two query fingerprints against the enrolled template. Construct a new virtual identity, combined fingerprint by reconstructing the combined minutiae template having similar topology with it. The Dual-Tree Complex Wavelet Transform (DTCWT) domain is used to thrashing the user identity. Experiment results show the robustness of the algorithm against image rotation and different types of image noises.

Accelerating Index-Based Audio Identification

In view of rapidly growing digital music collections and ubiquitous music consumption, the development of technologies for identifying, browsing, and managing audio content has become a major strand of research. In this context, audio identification (ID) systems for identifying audio recordings by means of short query audio clips have become of commercial relevance. In this paper, we take a closer look at a widely used audio ID system originally developed by Haitsma and Kalker and propose several modifications that yield significant improvements with regard to retrieval speed and storage requirements. As the main contribution, we introduce a measure that establishes a connection between the temporal correlation of hash values (used for indexing) and their ability to survive in the presence of noise and signal distortions. Based on this measure, we improve the overall performance of the audio ID system by means of four strategies. First, we change the way fingerprints (audio features) are generated to increase their reliability. Second, by prioritizing more reliable hash values when searching for reference entries, we achieve substantial gains in retrieval speed by a factor of almost seven. Third, by enlarging the query fingerprint, we increase our chances of identifying reliable hash values. Fourth, by indexing only the most reliable hashes, thus applying a sub-sampling strategy, we significantly lower the server side storage requirements by a factor of ten.

Fast source camera identification using matching signs between query and reference fingerprints.

Fast camera fingerprint search is an important issue for source camera identification in real-world applications. So far there has been little work done in this area. In this paper, we propose a novel fast search algorithm. We use global information derived from the relationship between the query fingerprint/digest and the reference fingerprints/digests in the database to guide fast search. This information can provide more accurate and robust clues for the selection of candidate matching database fingerprints. Because the quality of query fingerprints may degrade or vary in realistic applications, the construction of robust search clues is significant. To speed up the search process, we adopt a lookup table that is built on the separate-chaining hash table. The proposed algorithm has been tested using query images from real-world photos. Experiments demonstrate that our algorithm can well adapt to query fingerprints with different quality. It can achieve higher detection rates with lower computational cost than the traditional brute-force search algorithm and a pioneering fast search algorithm in literature.

Local and global structure preserving hashing for fast digital fingerprint tracing

Digital fingerprinting is a promising approach to protect multimedia contents from unauthorized redistribution. Whereas, large scale and high dimensionality make existing fingerprint detection methods fail to trace the traitors efficiently. To handle this problem, we propose a novel local and global structure preserving hashing to conduct fast fingerprint detection. This is the first work that introduces hash-based similarity search method to perform fingerprint detection. Applying the hashing method, we obtain a neighborhood-preserving low-dimensional representation (e. g. hash code) for each fingerprint. Through hash codes, we can find the nearest neighbors of the extracted fingerprint, thereby tracing the real traitors within a small range. Preserving the local structure facilitates to find the nearest neighbors of the query fingerprint efficiently, and preserving the global structure ensures hash codes of fingerprints as discriminative as possible. These properties make the proposed approach efficient to trace the real traitors. Extensive experiments demonstrate that the proposed approach outperforms traditional linear scan detection methods in term of efficiency.

Fingerprint indexing based on minutiae-dependent statistical codes

Methods based on minutiae matching have been extensively used in fingerprint recognition because minutiae can be reliably extracted from poor quality and noisy fingerprints. However, structures have to be defined due to minutiae displacements and irreproducibility. Some of the structures, though very efficient, incur large computational complexities. In this article, a feature vector of statistically based values derived from the minutiae pattern in a fingerprint is proposed for indexing fingerprints using the incremental search retrieval method. The proposed indexing technique in combination with the incremental retrieval method proves to have an added advantage over certain minutiae-based structures, especially when the minutiae points are numerous in a fingerprint. The feature vector for a fingerprint requires negligible storage resources and, consequently, the computational time in the retrieval of a candidate list for a query fingerprint is very little.

Fingerprint Combination for Privacy Protection

We propose here a novel system for protecting fingerprint privacy by combining two different fingerprints into a new identity. In the enrollment, two fingerprints are captured from two different fingers. We extract the minutiae positions from one fingerprint, the orientation from the other fingerprint, and the reference points from both fingerprints. Based on this extracted information and our proposed coding strategies, a combined minutiae template is generated and stored in a database. In the authentication, the system requires two query fingerprints from the same two fingers which are used in the enrollment. A two-stage fingerprint matching process is proposed for matching the two query fingerprints against a combined minutiae template. By storing the combined minutiae template, the complete minutiae feature of a single fingerprint will not be compromised when the database is stolen. Furthermore, because of the similarity in topology, it is difficult for the attacker to distinguish a combined minutiae template from the original minutiae templates. With the help of an existing fingerprint reconstruction approach, we are able to convert the combined minutiae template into a real-look alike combined fingerprint. Thus, a new virtual identity is created for the two different fingerprints, which can be matched using minutiae-based fingerprint matching algorithms. The experimental results show that our system can achieve a very low error rate with FRR = 0.4% at FAR = 0.1%. Compared with the state-of-the-art technique, our work has the advantage in creating a better new virtual identity when the two different fingerprints are randomly chosen.

OPTIMIZING THE ROTATION AND TRANSLATION OF FINGERPRINT IMAGES USING GENETIC ALGORITHM

In the minutiae-based fingerprint authentication system, the minutiae in the query image are required to be matched with the minutiae of the reference image that is stored in the database. Ideally, the minutiae extracted from the different impressions of the same fingerprint must match with each other, but practically, because of displacement, rotation, and other linear/ nonlinear distortions, minutiae extracted from different impressions of the same fingerprint do not match with each other. In order to maximize the number of matching minutiae, the alignment of the two fingerprints is required. Correctly aligning the fingerprints requires the translation and rotation to be recovered exactly. In this article, a new genetic-algorithm (GA)-based relative alignment algorithm for the alignment of reference and query fingerprint images is proposed. With the proposed algorithm there is no need to find the reference core or delta point because reliable detection of these reference points is a difficult task. In the proposed algorithm, all the three parameters x, y (translation), and θ (rotational) have been optimized separately. In order to improve the processing time, two acceleration steps have also been implemented. The experiments conducted on the FVC2002/Db1_a database reveal that a high accuracy has been achieved with the proposed method.