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Spoofing attacks on the fingerprint scanners become major and serious concern with the growing development and use of biometric technologies. Level 1 and Level 2 features; which are said to be unique and most commonly used features in fingerprint verification systems, are easily get spoofed. Moreover, single feature based specifically designed spoof detection methods are not performed well on different fingerprint scanners and spoofing materials. This paper proposed the fusion of pores perspiration and texture features in static software based approach to identify live and fake fingerprints. The pores perspiration activity is quantified by computing the ridge signal energy and gray level distributions around the detected pores. These pore characteristics are statically determined instead of dynamic measurement. Autoencoder neural network is used to reduce the high dimensional feature vector and learn its low dimensional hidden representation unsupervisedly. The binary classification in two classes: live and spoof is performed by the supervisedly trained softmax classifier. The performance of the classifier is evaluated in terms of Average Classification Error (ACE) and misclassification rates: FerrLive and FerrFake. The experimental results carried out on LivDet 2013 and LivDet 2015 databases show the improvement of the classifier performance in comparison to the state-of-the-art methods.

Fingerprint-based biometric systems have significant advantages over the conventional authentication systems, which are based on passwords and tokens. However, these systems are needed to combat the increasing magnitude of identity theft of users enrolled in a fingerprint-based biometric system because the fingerprint information of a user cannot be changed if it is compromised. Moreover, it has been demonstrated in the literature that a fingerprint image can be reconstructed if the information of minutiae points is available. In this paper, a fuzzy vault based technique is proposed to prevent identity theft and secure the fingerprint information (essentially, minutiae points) stored in the database. We propose a novel technique to filter the genuine vault points from a combination of genuine and chaff points used in the fuzzy vault technique. Since minutiae points are used to construct the vault, it is a challenging task to align probe and gallery images during verification. In order to do that, a Principal Component Analysis (PCA) based alignment technique is also proposed to align the gallery and probe templates. The proposed technique is evaluated on three different Fingerprint Verification Competition (FVC) databases that come under the FVC2002 and FVC2004. Subsequently, the obtained results are compared with that of the recent existing techniques in the literature and are found to be superior in terms of the Genuine Acceptance Rate (GAR), False Acceptance Rate (FAR), and Equal Error Rate (EER).

Biometric features are physical and biological characteristics that are unique to a person and can be used to accomplish authentication based on the particular modality. The main purpose of implementation of an Adaptive auto-correction technique for biometric time-attendance system is to improve the matching rate of fingerprint verification under the condition when fingerprint patterns vary due to environmental parameter like temperature. An Adaptive auto-correction technique is proposed which auto-corrects the reference fingerprint template at the time of genuine user rejection. The proposed technique is implemented on commercially available biometric device which uses Innovatrics, a standard commercially available extractor and matcher. Evaluation is carried out on 250 fingerprint templates of 10-users captured at varying temperature from 250C to 00C. The experimental analysis will be carried out to improve the recognition rate.

In this paper an authentication based finger print biometric system is proposed with personal identity information of name and birthday. A generation of National Identification Number (NIDN) is proposed in merging of finger print features and the personal identity information to generate the Quick Response code (QR) image that used in access system. In this paper two approaches are dependent, traditional authentication and strong identification with QR and NIDN information. The system shows accuracy of 96.153% with threshold value of 50. The accuracy reaches to 100% when the threshold value goes under 50.

The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms (PAD) into such systems. Companies and institutions need to know whether such integration would make the system more "secure" and whether the technology available is ready, and, if so, at what operational working conditions. Despite significant improvements, especially by adopting deep learning approaches to fingerprint PAD, current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modeling the cause-effect relationships when two non-zero error-free systems work together. Accordingly, this paper explores the fusion of PAD into verification systems by proposing a novel investigation instrument: a performance simulator based on the probabilistic modeling of the relationships among the Receiver Operating Characteristics (ROC) of the two individual systems when PAD and verification stages are implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms' ROCs submitted to the most recent editions of LivDet (2017-2019), the state-of-the-art NIST Bozorth3, and the top-level Veryfinger 12 matchers. Reported experiments explore significant scenarios to get the conditions under which fingerprint matching with embedded PAD can improve, rather than degrade, the overall personal verification performance.

Fingerprint recognition systems have been applied widely to adopt accurate and reliable biometric identification between individuals. Deep learning, especially Convolutional Neural Network (CNN) has made a tremendous success in the field of computer vision for pattern recognition. Several approaches have been applied to reconstruct fingerprint images. However, these algorithms encountered problems with various overlapping patterns and poor quality on the images. In this work, a convolutional neural network autoencoder has been used to reconstruct fingerprint images. An autoencoder is a technique, which is able to replicate data in the images. The advantage of convolutional neural networks makes it suitable for feature extraction. Four datasets of fingerprint images have been used to prove the robustness of the proposed architecture. The dataset of fingerprint images has been collected from various real resources. These datasets include a fingerprint verification competition (FVC2004) database, which has been distorted. The proposed approach has been assessed by calculating the cumulative match characteristics (CMC) between the reconstructed and the original features. We obtained promising results of identification rate from four datasets of fingerprints images (Dataset I, Dataset II, Dataset III, Dataset IV) with 98.1%, 97%, 95.9%, and 95.02% respectively by CNN autoencoder. The proposed architecture was tested and compared to the other state-of-the-art methods. The achieved experimental results show that the proposed solution is suitable for recreating a complex context of fingerprinting images.

Optical coherence tomography (OCT) is a high-resolution imaging technology probing the internal structure of multilayered tissues. Since it provides subsurface fingerprint information that is identical to the surface texture but unaffected by any surface defects, OCT-based fingerprints open up a new domain for establishing robust and high-security automatic fingerprint identification systems (AFISs). However, the development of OCT-based fingerprint recognition is hindered by the lack of public OCT-based fingerprint database for algorithm analysis and testing. This article, for the first time, established an OCT-based fingerprint database with thousands of fingers using our custom-built acquisition device. The website of this data set is https://github.com/CV-SZU/ . Moreover, the images included in the database were selected after quality evaluation based on image resolution, image size, effective measured area, and the number of extractable features. Finally, case studies, including antispoofing, multiple subsurface fingerprint reconstruction, and fingerprint verification, were discussed based on the developed database. The database can serve as a benchmark for developing effective antispoofing, live detection, and high-accurate fingerprint recognition algorithms. It will significantly promote the research in the area of fingerprint biometric and will also advance the development of commercial products.

Fingerprints are the one of the most important means in the forensics as a means of identification of the criminals owning to the uniqueness and the distinct features in them. Fingerprint identification is considered as an important means for the identification of the people around the globe. Minutiae are the details present in the human fingerprints which are used as a means of identification and verification. Minutiae are the distinctive points which can be used for the effective reconstruction of the fingerprint image. However, there was a limitation that was considered. The minutiae points are completely not enough for reconstruction of the image. Many spurious minutiae are not included and the results for the latent fingerprints are not as accurate as they are for the normal data sets. In this paper, a novel technique has been proposed which considers the minutiae density and the orientation field direction for the reconstruction of the fingerprint. Two public domain databases Fingerprint verification competition 2002 (FVC2002) and fingerprint verification competition 2004 (FVC2004) have been used for the experimental results and to validate the suggested methods for the fingerprint reconstruction and enhancement.

Fingerprint registration and verification is an active area of research in the field of image processing. Usually, fingerprints are obtained from sensors; however, there is recent interest in using images of fingers obtained from digital cameras instead of scanners. An unaddressed issue in the processing of fingerprints extracted from digital images is the angle of the finger during image capture. To match a fingerprint with 100% accuracy, the angles of the matching features should be similar. This paper proposes a rotation and scale-invariant decision-making method for the intelligent registration and recognition of fingerprints. A digital image of a finger is taken as the input and compared with a reference image for derotation. Derotation is performed by applying binary segmentation on both images, followed by the application of speeded up robust feature (SURF) extraction and then feature matching. Potential inliers are extracted from matched features by applying the M-estimator. Matched inlier points are used to form a homography matrix, the difference in the rotation angles of the finger in both the input and reference images is calculated, and finally, derotation is performed. Input fingerprint features are extracted and compared or stored based on the decision support system required for the situation.

Due to the availability of fingerprint verification technology on mobile devices, fingerprint based human identification has become the most widely used biometric technology in everyday life. The elderly find digital payment applications difficult to operate and unsecure. This work has exploited fingerprint verification availability on mobile devices to provide a user friendly and secure digital wallet payment facility to elderlies who are unable to avail this facility due to the complex infrastructure of traditional authentication mechanisms. A novel digital payment mechanism is presented which uses Bluetooth technology of mobile devices for billing at the point of sale and fingerprint verification for user authentication. A proof of the concept study is presented to validate the proposed model using state of the art usability questionnaires and attributes with the target group. The result indicates that the proposed methodology provides satisfaction and ease of use to the user.

In this paper, we propose a novel biometric cryptosystem for vectorial biometrics called symmetric keyring encryption (SKE), inspired by Rivest's keyring model (2016). Unlike conventional biometric secret-binding primitives, such as fuzzy commitment and fuzzy vault approaches, the proposed scheme reframes the biometric secret-binding problem as a fuzzy symmetric encryption problem using a concept called a resilient vector pair. In this study, this pair resembles the encryption–decryption key pair in symmetric key cryptosystems. This scheme is realized using an index of maximum hashed vectors, a special instance of the ranking-based locality-sensitive hashing function. With a simple filtering mechanism and an [m, k] Shamir's secret-sharing scheme, we show that SKE, both in theory and in an empirical evaluation, can retrieve the exact secret with overwhelming probability for a genuine input yet negligible probability for an imposter input. Although SKE can be applied to any vectorial biometrics, we adopt fingerprint and face vectors in this work. Experiments were performed using the Fingerprint Verification Competition (FVC) and Labeled Face in the Wild (LFW) datasets. We formalize and analyze the threat model for SKE, which involves several major security attacks.

In this study, a novel method based on deep learning for aligned fingerprints matching is proposed. According to the characteristics of fingerprint images, a convolutional network, Finger ConvNet, is designed. In addition, a new joint supervision signal is used to train Finger ConvNet to obtain deep features. Experimental studies are performed on public fingerprint datasets, the ID Card fingerprint dataset and the Ten-Finger Fingerprint Card fingerprint dataset. Furthermore, four performance indicators, the false matching rate (FMR), false non-matching rate (FNMR), equal error rate (EER) and receiver operating characteristic (ROC) curve, are measured. The experimental results demonstrate the effectiveness of the proposed method, which achieved a competitive effect in comparison with conventional fingerprint matching algorithms in fingerprint verification tasks using the FVC2000, FVC2002, and FVC2004 datasets. Moreover, the matching speed of the proposed method was almost 5 times faster than the fastest conventional fingerprint matching algorithms. In addition, it can be used as a fast matching method to filter out many templates with low scores by setting a threshold according to the matching scores and thus accelerate the process in identification tasks.

Fingerprint classification is vital for reducing the search time and computational complexity of the fingerprint identification system. The robustness of classifier relies on the strength of extracted features and the ability to deal with low-quality fingerprints. The proficiency to learn accurate features from raw fingerprint images rather than explicit feature extraction makes deep convolutional neural networks (DCNNs) attractive for fingerprint classification. The DCNNs use softmax for quantifying model confidence of a class for an input fingerprint image to make a prediction. However, the softmax probabilities are not a true representation of model confidence and often misleading in feature space that may not be represented with the available training examples. The primary goal of this study is to improve the efficacy of the fingerprint classification by dealing with false positives by employing Bayesian model uncertainty. The efficacy of the proposed method is shown through experimentations on NIST special database 4 (NIST-4) and fingerprint verification competition 2002 database 1-A (FVC DB1-A) 2002 and 2004 datasets. Results show that 0.8-1.0% of accuracy is improved with model uncertainty over the conventional DCNN.

Aims: To provide a baseline for the configuration of Automated Fingerprint Verification System (AFVS) in the face of changing weather and environmental conditions in order to ensure performance accuracy. Study Design: Statistical and theoretical research approaches. Place and Duration of Study: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria, between July 2017 and July 2018. Methodology: Data set were collected in the South-South geopolitical zone of Nigeria. We use 10,000 minutiae points defined by location and orientation features extracted from fingerprint samples obtained at 9 various physical and environmental conditions over 12 months period. These data were used to formulate linear regression models that were used as constraints to the verification objective function derived as constrained linear least squares. The effects of the changing weather and environmental conditions were incorporated into the optimised point-set matching model in order to minimise the total relative error on location and orientation differences between pairs of minutiae. The model was implemented using interior-point convex quadratic programming was implemented in Matlab. Results: The results obtained from the optimisation function by adjusting the thresholds of the effects of weather and environmental conditions to 0.0, 0.0 for location and orientation properties of minutiae, respectively, showed minimal total relative errors on the corresponding pairs of matched minutiae, when compared with using the default threshold values of the selected conditions. Conclusion: The optimisation of point-set based model could provide a computational basis for accurate fingerprint verification for low and high-security AFVS in unfavourable conditions if they are incorporated into the matching model. However, further validation and evaluation of the model with data sets from regions with similar weather and environmental conditions is needed to further validate its robustness in terms of performance accuracy

Fingerprint minutiae information is an unordered and variable-sized collection of minutiae locations and orientations. Advanced template protection algorithms which require a fixed-length binary template cannot operate on minutiae points. In this paper, we propose a novel framework that provides practical solutions that can be used in developing secure fingerprint verification systems. The framework, by using a GMM-SVM fingerprint representation scheme, first generates fixed-length feature vectors from minutiae point sets. The fixed-length representation enables the application of modern cryptographic alternatives based on homomorphic encryption to minutiae template protection. Our framework then utilizes an asymmetric locality sensitive hashing (ALSH) in order to convert the generated fixed-length but real valued GMM-SVM feature vector to a binary bit string. This binarization step transforms the matching process to calculating Hamming distance between binary vectors and expedites fingerprint matching. The verification performance of the framework is evaluated on FVC2002DB1A and DB2A databases.

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.

Fingerprint recognition is one of the most used biometric system in these days. Different methods used for these are using touch sensors, optical sensors or capacitive sensors. Each one has their own advantages and disadvantages. But all these need to have a physical contact of the finger with the sensor leading to hygienic issues and inaccuracy. Some problems that people face in current fingerprint authentication systems are caused due to smudge marks and oily fingers. To overcome these problems, the concept of contactless fingerprint recognition system can be used. It uses image processing and deep learning model implemented in the form of an app. Usage of public property needs to be minimized for avoiding physical contact and sanitation. Instead of a common instrument, this can be simplified into an application for every user. Using Machine Learning and in-built camera, the instrument can be reciprocated into an application. Since everyone has a smartphone nowadays, the impact of this project would be immense. Hence instead of utilizing a common instrument, this application can be used to do the same, provided they are connected to the same server network.

Biometric recognition systems provide an easy way to verify an individual’s identity through physiological and behavioral biometric traits, due to the persistence of these traits. The physiological traits are extensively utilized to secure the applications of numerous fields. Among these, toe print is one of the physiological traits that has been discussed and evaluated for the children; however, it has not been addressed in the past as this can also be utilized to verify the identity of an adult, especially for a person with different abilities. In this paper, we have come up with a feasibility study of toe prints while comparing them with the impression of a person’s different fingers in terms of the overall performance of a biometric verification system. To accomplish this task, the toe print database is collected from different persons of age varying from 17 years to 64 years, which is the first database of its kind and is made available in public domain. Fingerprint impressions are also collected along with the toe print to compare the reliability of the toe print with respect to performance using a standard fingerprint verification tool. Verifinger trial version has been utilized by considering the two standard fingerprint verification protocols viz. 1-vs-1 and FVC to assess the performance of the toe print verification system in terms of the equal error rate (EER). The toe print verification system attains 0.04% and 0.01% EER values for 1-vs-1 and FVC protocol, respectively, which clearly depicts the feasibility of toe print as a potential biometric trait.

Latent fingerprints are the fingerprint traces obtained from various surfaces of objects found at the crime scene. Latent fingerprint quality is, therefore, poor and they suffer from non-linear distortions. This is why latent fingerprints contain partial or incomplete minutia information. Developing an automatic fingerprint identification system (AFIS) for latent fingerprints becomes a major challenge because of a small number of minutia features. Moreover, currently developed AFIS for latent fingerprints are not robust against geometric transformations. In this article, based on the existing partial minutiae characteristics, we propose a method for detecting latent fingerprints. To identify an individual with existing nearest neighbor minutia structures, we develop a scale and rotation invariant algorithm called “Ratio of Minutiae Triangles (RMT)”. The algorithm utilizes the features which are based on the local minutiae arrangements around a reference minutia. To deal with missing minutiae in latent fingerprints, we consider minutiae clusters based on nearest minutiae neighborhood relation to form hash structures. In the fingerprint retrieval process, these hash structures are used. On the FVC2004 (Maio et al., FVC2004: third fingerprint verification competition. In: International Conference on Biometric Authentication. ICBA, Hong Kong, pp 1–7, 2004) and NIST SD27 (Garris and Mccabe, NIST Special Database 27: Fingerprint Minutiae from Latent and Matching Tenprint Images, Technical Report. National Institute of Standards and Technology, Gaithersburg, 2000) databases, experiments are carried out with different rotation and scale settings. For the FVC2004 and NIST SD27 datasets, we achieved Rank-1 identification accuracy of 78.75% and 86.82%, respectively. The result demonstrates substantial changes in the matching results relative to state-of-the-art algorithms, and the proposed method is robust against geometric transformations.

We investigated the threat level of realistic attacks using latent fingerprints against sensors equipped with state-of-art liveness detectors and fingerprint verification systems which integrate such liveness algorithms. To the best of our knowledge, only a previous investigation was done with spoofs from latent prints. In this paper, we focus on using snapshot pictures of latent fingerprints. These pictures provide molds, that allows, after some digital processing, to fabricate high-quality spoofs. Taking a snapshot picture is much simpler than developing fingerprints left on a surface by magnetic powders and lifting the trace by a tape. What we are interested here is to evaluate preliminary at which extent attacks of the kind can be considered a real threat for state-of-art fingerprint liveness detectors and verification systems. To this aim, we collected a novel data set of live and spoof images fabricated with snapshot pictures of latent fingerprints. This data set provide a set of attacks at the most favourable conditions. We refer to this method and the related data set as "ScreenSpoof". Then, we tested with it the performances of the best liveness detection algorithms, namely, the three winners of the LivDet competition. Reported results point out that the ScreenSpoof method is a threat of the same level, in terms of detection and verification errors, than that of attacks using spoofs fabricated with the full consensus of the victim. We think that this is a notable result, never reported in previous work., Comment: Accepted for the 25th International Conference on Pattern Recognition (ICPR 2020)

A fingerprint Region of Interest (ROI) segmentation module is one of the most crucial components in the fingerprint pre-processing pipeline. It separates the foreground finger-print and background region due to which feature extraction and matching is restricted to ROI instead of entire finger-print image. However, state-of-the-art segmentation algorithms act like a black box and do not indicate model confidence. In this direction, we propose an explainable finger-print ROI segmentation model which indicates the pixels on which the model is uncertain. Towards this, we benchmark four state-of-the-art models for semantic segmentation on fingerprint ROI segmentation. Furthermore, we demonstrate the effectiveness of model uncertainty as an attention mechanism to improve the segmentation performance of the best performing model. Experiments on publicly available Fingerprint Verification Challenge (FVC) databases show-case the effectiveness of the proposed model.

The security of messaging applications against person-in-the-middle attacks relies on the authenticity of the exchanged keys. For users unable to meet in person, a manual key fingerprint verification is necessary to ascertain key authenticity. Such fingerprints can be exchanged visually or verbally, and it is not clear in which condition users perform best. This paper reports the results of a 62-participant study that investigated differences in performance and perceived usability of visual and verbal comparisons of word-based key fingerprints, and the influence of the individual’s cognitive learning style. The results show visual comparisons to be more effective against non-security critical errors and are perceived to provide increased confidence, yet participants perceive verbal comparisons to be easier and require less mental effort. Besides, limited evidence was found on the influence of the individual’s learning style on their performance.

One of the open issues in fingerprint verification is the lack of robustness against image-quality degradation. Poor-quality images result in spurious and missing features, thus degrading the performance of the overall system. Therefore, it is important for a fingerprint recognition system to estimate the quality and validity of the captured fingerprint images. In this work, we review existing approaches for fingerprint image-quality estimation, including the rationale behind the published measures and visual examples showing their behavior under different quality conditions. We have also tested a selection of fingerprint image-quality estimation algorithms. For the experiments, we employ the BioSec multimodal baseline corpus, which includes 19200 fingerprint images from 200 individuals acquired in two sessions with three different sensors. The behavior of the selected quality measures is compared, showing high correlation between them in most cases. The effect of low-quality samples in the verification performance is also studied for a widely available minutiae-based fingerprint matching system., Comment: Published at IEEE Transactions on Information Forensics and Security

Partial fingerprint verification is a challenging task because of the few features contained in small area as well as the large rotation angle and translation between query images and template images. In this paper, we propose a new framework of partial fingerprint verification based on spatial transformer networks (STN) model, where a transform model, i.e., AlignNet network, is proposed to estimate the alignment parameters, and the verification is modeled as a binary classification task. The experimental results on the simulated datasets created from FVC2004 and the real-world dataset FVC2006 DB1 show that our method is invariant to rotation, and also robust to different kinds of scanners, and dramatically outperforms the rank-1 entry of FVC2006 participants. The EER on FVC2006 DB1 of the proposed algorithm is 3.587% compared to that of 5.564%, the best of FVC2006 DB1 entries.

International audience; Due to the advantages in privacy and efficiency requirements, minutiae template based matching is the dominant technique among the authentication approaches of fingerprint image and its performance fully relies on the quality of the input fingerprint image. In this case, it is reasonable to consider qualifying fingerprint with fingerprint minutiae template information extracted from fingerprint image, particularly when using for embedded applications due the limited memory. In fact, the speed of fingerprint recognition increases with the decrease of the size of database. For these reasons, a new confidence measure called Minutia Confidence Index (MiCI) for each minutia of the template is proposed. This index predicts the importance and the usefulness of each minutia with respect to the others in the template. It takes into account only minutiae template information (i.e., x and y coordinates, the type and the orientation). MiCI score is a value between 0 and 1, where highest values are for the mostly relevant minutiae in the template whereas lowest values are for less important ones. This measure has been applied in the template reduction use case on Fingerprint Verification Competition (FVC) and SFINGE0 databases and demonstrated its capability to reach high performance.

We investigated false-rejection problems of the active fingerprint verification system. We found that the system denies persons who own his/her identities because of his/her improper impression conditions, growth conditions, and skin conditions. These false-rejection problems resulted in a time-delay, human assistance requirement, and further investigation. We performed fingerprint pre-enhancement to improve the quality of input to alleviate these false-rejection problems. We employed five fingerprint enhancement algorithms from previously published works and one from the industry for the possibility of reducing the rejection rate. The experimental results show that the enhancement method from the industry can maximally reduce the rejection rate by 22%.

This paper presents a solution for a small area (partial-to-partial) fingerprint verification, which is suitable for mobile devices with small fingerprint sensors. Since images captured from the fingerprint sensor contain very limited minutiae features and less image overlapping areas between the input image and the template image, the minutiae-based method is hardly used. We propose a new approach that consists of two steps:(l) fingerprint alignment using band-limited phase-only correlation (BLPOC) and (2) fingerprint matching using deep convolutional neural network (DCNN). To handle the large displacement between two fingerprints, we added both preprocessing and post-processing steps to make the fingerprint alignment process more robust. The experiment results show that the proposed method achieves better performances compared to other methods.

The existing automatic fingerprint verification methods are designed to work under the assumption that the same sensor is installed for enrollment and authentication (regular matching). There is a remarkable decrease in efficiency when one type of contact-based sensor is employed for enrolment and another type of contact-based sensor is used for authentication (cross-matching or fingerprint sensor interoperability problem). The ridge orientation patterns in a fingerprint are invariant to the sensor type. Based on this observation, we propose a robust fingerprint descriptor called the co-occurrence of ridge orientations (Co-Ror), which encodes the spatial distribution of ridge orientations. Employing this descriptor, we introduce an efficient automatic fingerprint verification method for cross-matching problem. Furthermore, to enhance the robustness of the method, we incorporate scale-based ridge orientation information through the Gabor-HoG descriptor. The two descriptors are fused with the canonical correlation analysis (CCA), and the matching score between two fingerprints is calculated using city-block distance. The proposed method is alignment-free and can handle the matching process without the need for a registration step. The intensive experiments on two benchmark databases (FingerPass and MOLF) show the effectiveness of the method and reveal its significant enhancement over the state-of-the-art methods, such as VeriFinger (a commercial SDK), minutia cylinder-code (MCC), MCC with scale, and the thin-plate spline (TPS) model. The proposed research will help security agencies, service providers, and law-enforcement departments to overcome the interoperability problem of contact sensors of different technologies and interaction types.

Post-mortem fingerprints are a valuable biometric used to aid in the identification of a deceased individual. However, fingerprints from the deceased undergo decomposition leading to indefinite structure when compared to ante-mortem fingerprints. Moreover, the performance of the existing two-dimensional (2D) fingerprint recognition systems is still below the expected potential. These problems arise because fingerprints are generally captured by manipulating a finger against a plane. In post-mortem fingerprint recovery, the decedent's finger must go through several reconditioning processes to prevent the rapid onslaught of decomposition. To address these deficiencies associated with the 2D systems, three-dimensional (3D) scanning systems have been employed to capture fingerprints. The 3D technology is still in its transient phase and is limited primarily by 1) the lack of existing 3D databases; 2) the deficiency of 3D-to-2D fingerprint image mapping algorithms, 3) the incapacity to model and recreate the 2D fingerprint capturing procedure to improve 3D-2D fingerprint verification; and 4) the inability to apply traditional fingerprint unrolling techniques on post-mortem 3D fingerprints. This paper presents a novel method to perform post-mortem 3D fingerprint unrolling and pressure simulation to produce fingerprint images that are compatible with 2D fingerprint recognition systems. The thrust of this paper strives to: 1) develop a correspondence between 3D touchless and contact-based 2D fingerprint images; 2) model fingerprints with deformities to provide a viable fingerprint image for matching and; 3) develop a mosaic pressure simulation (MPS) algorithm to recreate the effects of 2D fingerprint capturing procedure.

Latent fingerprints are acquired from crime places which are utilized to distinguish suspects in crime inspection. In general, latent fingerprints contain mysterious ridge and valley structure with nonlinear distortion and complex background noise. These lead to fundamentally difficult problem for further analysis. Hence, the image quality is required for matching those latent fingerprints. In this work, we develop a model for enhancement of latent fingerprint and matching algorithm, which requires manually marked (ground-truth) ROI latent fingerprints. This proposed model includes two phases (i) Latent fingerprints contrast enhancement using type-2 intuitionistic fuzzy set (ii) Extract the minutiae and Scale Invariant Feature Transformation (SIFT) features from the latent fingerprint image. For matching, these algorithms have been figured based on minutiae and SIFT points which inspect n number of images and the scores are calculated by Euclidean distance. We tested our algorithm for matching, using some public domain fingerprint databases such as Fingerprint Verification Competition − 2004 (FVC-2004) and Indraprastha Institute of Information Technology (IIIT)-latent fingerprint which indicates that by fusing the proposed enhancement algorithm, the matching precision has fundamentally moved forward.

In spite of the benefits of biometric-based authentication systems, there are few concerns raised because of the sensitivity of biometric data to outliers, low performance caused due to intra-class variations, and privacy invasion caused by information leakage. To address these issues, we propose a hybrid fusion framework where only the protected modalities are combined to fulfill the requirement of secrecy and performance improvement. This paper presents a method to integrate cancelable modalities utilizing Mean-Closure Weighting (MCW) score level and Dempster-Shafer (DS) theory based decision level fusion for iris and fingerprint to mitigate the limitations in the individual score or decision fusion mechanisms. The proposed hybrid fusion scheme incorporates the similarity scores from different matchers corresponding to each protected modality. The individual scores obtained from different matchers for each modality are combined using MCW score fusion method. The MCW technique achieves the optimal weight for each matcher involved in the score computation. Further, DS theory is applied to the induced scores to output the final decision. The rigorous experimental evaluations on three virtual databases indicate that the proposed hybrid fusion framework outperforms over the component level or individual fusion methods (score level and decision level fusion). As a result, we achieve (48%, 66%), (72%, 86%) and (49%, 38%) of performance improvement over unimodal cancelable iris and unimodal cancelable fingerprint verification systems for Virtual_A, Virtual_B, and Virtual_C databases, respectively. Also, the proposed method is robust enough to the variability of scores and outliers satisfying the requirement of secure authentication.

Biometrics authentication is considered as most secure and reliable method to recognise and identify person's identity. Researchers put efforts to find efficient ways to secure and classify the solutions to biometric problems. In this category, fingerprint recognition (FPR) is most widely used biometric trait for person identification/verification. The present work focuses an FPR technique, which uses the grey-level difference method, discrete wavelet transforms and edge histogram descriptor for fingerprint representation and matching. Wavelet shrinkage used for noise removal in the image. Ridge flow estimation is calculated using the gradient approach. SVM and Hamming distance similarity measures are used for recognition. The experiment result has been tested on the standard 2000–2004 fingerprint verification competition dataset and the accuracy of proposed algorithm was reported to be well above 98%.

Door locks and user authentication are the major issues in the current banking industry. There are a number of user future computing techniques, and security is especially essential to these methods. Existing digital door locks have the problem of opening easily with a stolen PIN number or by electrical shock. Thus, this study proposed and implemented an integrated automatic log-in platform based on mobile fingerprint recognition by applying the blockchain theory. As a result of this research, a convenient, integrated automatic log-in platform with powerful security has been constructed using a smartphone-based fingerprint recognition function. There are three major functions of the platform. First, it is possible to authenticate the user in PC, mobile device, and IoT environments through fingerprint recognition. Second, the platform includes SDK to develop application software for user authentication and IoT services. Last is its strengthened security using the blockchain theory to prepare against tampering/forging/leaking of a user’s fingerprint information by hackers.

In this paper, we present a multibiometric system based on two different biometric modalities, palmprint and fingerprint. The proposed system is based on a score fusion which can effectively combine the discriminative power of multiple biometric traits. We propose a combined fingerprint image enhancement, which achieves satisfactory results. We also apply two different indexing algorithms on fingerprints and three different indexing algorithms on palmprints. Existing multibiometric systems are not sufficiently fast for practical applications. Indexing and retrieval mechanisms can effectively narrow down the search space in large databases. We propose a fingerprint indexing based on ellipse properties, which reduces false correspondences. The proposal uses a k-means clustering algorithm to index and retrieve fingerprints and palmprints. Furthermore, we combine some minutia cylinder-code (MCC)-based algorithms and improve the MCC-based indexing algorithms. Empirical experiments on some of datasets of the national institute of standards and technology (NIST), fingerprint verification competition (FVC), and THUPALMLAB databases show that the proposed algorithm considerably narrows down the search space in fingerprint and palmprint databases and increase the recognition performance.

Latent fingerprints have attracted considerable attention from researchers in the fields of forensics and law enforcement applications. Public demand for these applications may be the driving force behind further progress in biometrics research. Although great effort has been taken to devise algorithms for overlapped latent fingerprint classification system, there are still many challenging problems involved in fingerprint classification systems. Most of the fingerprint-based applications will prolong with fingerprint recognition because of its proven performance, the existence of large databases and the availability of the fingerprint devices with minimum cost. There are various issues that need to be addressed to develop fingerprint classification system. In this connection, there are some designing challenges such as nonlinear distortion, low-quality image, segmentation, sensor noise, skin conditions, overlapping, inter-class similarity, intra-class variations and template ageing. In crime scenes, the latent images can be overlapped with some background images or more number of fingerprint images from same person or different person. An overlapped fingerprint image should be processed for fingerprint classification. This proposed recognition system suggests multiple criteria decision analysis technique to assess the overlapped latent fingerprints. The proposed multiple criteria such as first-order, second-order and third-order features are used to classify the overlapped latent fingerprints. The proposed system designs the novel classification system for overlapped latent fingerprint images using ANFIS classifier. Extensive experiments are performed on the simultaneous latent fingerprint databases, and National Institute of Standards and Technology-Special Database 27, Fingerprint Verification Competition 2006 Database1-A and Database2-A databases. The planned work enables accurate and fast data retrieval by using one-to-N fingerprint classification for overlapped latent images. The experimental results are highly promising, and they outperform the existing systems in classifying overlapped images. The performance of adaptive neuro fuzzy inference system classifier is evaluated by applying the k-fold cross-validation technique. The outcome of the work shows that the overlapped fingerprint is classified in a successful manner, and the results are compared with Bayes, SVM and MLP classifiers. The obtained results show that the proposed system achieves a better classification rate of 90.66% with 5 s, 86.66% with 12 s compared to the existing system.

The emergence of electronic health or eHealth has revolutionized the healthcare industry to offer better healthcare services at a low and affordable cost. However, it still suffers from security an...

The fingerprint is one of the oldest and most widely used biometric modality for person identification. Existing automatic fingerprint matching systems perform well when the same sensor is used for both enrollment and verification (regular matching). However, their performance significantly deteriorates when different sensors are used (cross-matching, fingerprint sensor interoperability problem). We propose an automatic fingerprint verification method to solve this problem. It was observed that the discriminative characteristics among fingerprints captured with sensors of different technology and interaction types are ridge orientations, minutiae, and local multi-scale ridge structures around minutiae. To encode this information, we propose two minutiae-based descriptors: histograms of gradients obtained using a bank of Gabor filters and binary gradient pattern descriptors, which encode multi-scale local ridge patterns around minutiae. In addition, an orientation descriptor is proposed, which compensates for the spurious and missing minutiae problem. The scores from the three descriptors are fused using a weighted sum rule, which scales each score according to its verification performance. Extensive experiments were conducted using two public domain benchmark databases (FingerPass and Multi-Sensor Optical and Latent Fingerprint) to show the effectiveness of the proposed system. The results showed that the proposed system significantly outperforms the state-of-the-art methods based on minutia cylinder-code (MCC), MCC with scale, VeriFinger-a commercial SDK, and a thin-plate spline model.

This paper focuses on preventing car theft using microcontrollers and GSM modules. We are generating the results with better accuracy and proficiency. It can be helpful to those people who want better and more advanced security in their vehicle.

A novel and efficient fingerprint indexing based on minutiae triplets, is proposed.The proposed fingerprint features are invariant to rotation and translation.Our fingerprint representation is very robust to distortions and noise.The experimental results demonstrate the validity of the proposed algorithm. Fingerprint indexing plays a key role in the automatic fingerprint identification systems (AFISs) which allows us to speed up the search in large databases without missing accuracy. In this paper, we propose a fingerprint indexing algorithm based on novel features of minutiae triplets to improve the performance of fingerprint indexing. The minutiae triplet based feature vectors, which are generated by ellipse properties and their relation with the triangles formed by the proposed expanded Delaunay triangulation, are used to generate indices and a recovery method based on k-means clustering algorithm is employed for fast and accurate retrieval. The proposed expanded Delaunay triangulation algorithm is based on the quality of fingerprint images and combines two robust Delaunay triangulation algorithms. This paper also employs an improved k-means clustering algorithm which can be applied over large databases, without reducing the accuracy. Finally, a candidate list reduction criteria is employed to reduce the candidate list and to generate the final candidate list for matching stage. Experimental results over some of the fingerprint verification competition (FVC) and national institute of standards and technology (NIST) databases show superiority of the proposed approach in comparison with state-of-the-art indexing algorithms. Our indexing proposal is very promising for the improvement of real-time AFISs efficiency and accuracy in the near future.

An efficient fingerprint indexing, which uses ellipse properties, is proposed.A new fingerprint indexing is proposed using minutiae pairs and convex core point.Our fingerprint representation is very robust to distortions and noise.The proposed fingerprint features are invariant to rotation and translation. Fingerprint identification is an important issue for identifying fingerprints and plays a key role in the fingerprint recognition systems. However, performing a fingerprint identification over a large database can be an inefficient task due to the lack of scalability and high computing times of fingerprint matching algorithms. Fingerprint indexing is a key strategy in automatic fingerprint identification systems (AFISs) which allows us to reduce the number of candidates, the search space, and the occurrences of false acceptance in large databases. In this paper, an efficient indexing algorithm is proposed using minutiae pairs and convex core point which employs k-means clustering and candidate list reduction criteria to improve the identification performance. Our proposal can effectively reduce the search space and number of candidates for fingerprint matching, and thus achieves higher efficiency and significantly improves the system retrieval performance. Experimental results over some of the fingerprint verification competition (FVC) and the national institute of standards and technology (NIST) databases prove the superiority of the proposed approach against some of the well known indexing algorithms.

In this paper, a fingerprint image encryption algorithm is proposed in order to enhance the protection of fingerprint-based systems against replay attacks. The proposed algorithm is consisting of permutation and diffusion operations in wavelet domain, whereas, one-level Lifting Wavelet Transform Integer-to-Integer is performed to the original fingerprint image. The approximation and detail sub-bands are then partitioned into blocks and permuted using a permutation key. It is noteworthy that, for each sub-band the Rubik’s cube principle is applied. The encrypted image is constructed by ordering the encrypted sub-bands. Eventually, an experimental tests and security analysis were conducted on three fingerprint images attained through Fingerprint Verification Competition “FVC 2000” database. The obtained results confirm the effectiveness of the proposed encryption algorithm and clearly show the robustness against common attacks, for example differential and statistical attacks. In addition, it reveals the high security level achieved.

In an era of advanced computer technology world where innumerable services such as access to bank accounts, or access to secured data or entry to some national important organizations require authentication of genuine individual. Among all biometric personal identification systems, fingerprint recognition system is most accurate and economical technology. In this paper we have proposed fingerprint recognition system using Local Walsh Hadamard Transform (LWHT) with Phase Magnitude Histograms (PMHs) for feature extraction. Fingerprints display oriented texture-like patterns. Gabor filters have the property of capturing global and local texture information from blur or unclear images and filter bank provides the orientation features which are robust to image distortion and rotation. The LWHT algorithm is compared with other two approaches viz., Gabor Coefficients and Directional Features. The three methods are compared using FVC 2006 Finger print database images. It is found from the observation that the values of TSR, FAR and FRR have improved results compared to existing algorithm.

Objectives: This paper is focused with original features used in fingerprint matching founded on back propagation neural network. For finding the features in the images, it can be improved using filtering techniques as isotropic and anisotropic. Isotropic can protect features on the input images but can barely progress the dominance of the images. But on the contrary, anisotropic filtering can successfully eliminate noise from the image just when a consistent point of reference is provided. Methods and Analysis: The filters commonly used namely median filter, gabor filter along with anisotropic filters are used for filtering the noises under direct gray scale enhancement .Whereas for an input image, the narrow ridge direction is predictable and the region of interest is positioned. The uniqueness of a fingerprint is exclusively determined by the local ridge characteristics and their relationships. The ridges and valleys in a fingerprint alternate, flowing in a local constant direction. The two most prominent local ridge characteristics are: 1) ridge ending and, 2) ridge bifurcation. A ridge ending is defined as the point where a ridge ends rapidly. A ridge bifurcation is defined as the point where a ridge forks or diverges into branch ridges. Collectively, these features are called minutiae. Minutiae points are extracted during the enrollment process and then for each authentication. In a fingerprint, they correspond to either a ridge ending or a bifurcation. Minutiae are major features of a fingerprint using which comparisons of one print with another can be made. After finding the minutiae points the filters reducing image noises, smoothing, removing some forms of misfocus and motion blur, is in the front step of image processing. Filtering is also used for preserving the true ridge and valley structures. Findings: The digital results of these features are practically feeded as input of the neural network using median filter, gabor filter, anisotropic filter for training function. For fingerprint identification the confirmation part of the system identifies the fingerprint based training show of the network. Novelty and Improvement: To finish the new outcome reveals that the number of accepted sample rate of the proposed method using the three filters which is far better than the existing fingerprint verification system using artificial neural network

Currently, an attempt is being made to introduce biometric technologies in various spheres of public and state life: forensics, access control systems, applications on mobile devices, banking, etc. The problem of accuracy remains an open question for discussion, because when solving the problem of verification of biometric samples there are problems of addition or disappearance of reference points, deformation of distances between them, linear and angular displacements of the whole sample. Also, the developed biometric systems do not meet all the requirements of information security, namely the integrity, accessibility, authenticity, indisputability, observability and confidentiality. The article presents an analysis of the method of decomposition of minefields during fingerprint verification, describes its advantages and disadvantages in comparison with other methods. It is based on the creation of local structures for each minute of the imprint, because it is the local structures that are resistant to mixing, angular and linear displacement of points. Building global structures often does not lead to good accuracy, as there is a problem of centering the entire sample. A complete list of tests of samples of the database of fingerprints during their verification by this method. An algorithm for constructing a code for an arbitrary minution and an algorithm for comparing two sample templates are described. The results of the article show the value of pairwise comparisons of two templates for true and false tests. The indicators of false rejection rate (FRR), false access rate (FAR), single equivalent error rate (EER) were studied.

In recent years, for verification and identification, uses of biometrics information are increased rapidly because they are more secure and reliable. Biometrics (face, finger, iris, palm, etc.) recognize the person based on human traits that are physiological and behavioral traits. Fingerprint recognition systems are widely used biometric for verification and identification due to its universality in nature and easiness. But they are various types of attacks are present that affect the performance of the fingerprint recognition system like spoofing attacks, displacement error, and physical distortion, etc. In this proposed system, work is carried out to overcome these types of errors and enhances the accuracy of the system. For spoofing detection, supervised learning with minutiae extraction method is used, for displacement error, alternating direction method multiplier (ADMM) is used and enhance the accuracy of the system by a technique that uses crossing number for minutiae extraction, for feature extraction gray-level difference method, discrete wavelet transforms, and feature matching using hamming distance. For learning and classification, support vector machine is used. In this fingerprint verification competition (FVC) 2002, FVC2004, FVC2006, and ATVS are considered for testing purpose and calculation of accuracy.

Although most fingerprint matching methods utilize minutia points and/or texture of fingerprint images as fingerprint features, the frequency spectrum is also a useful feature since a fingerprint is composed of ridge patterns with its inherent frequency band. We propose a novel CNN-based method for extracting fingerprint features from texture, minutiae, and frequency spectrum. In order to extract effective texture features from local regions around the minutiae, the minutia attention module is introduced to the proposed method. We also propose new data augmentation methods, which takes into account the characteristics of fingerprint images to increase the number of images during training since we use only a public dataset in training, which includes a few fingerprint classes. Through a set of experiments using FVC2004 DB1 and DB2, we demonstrated that the proposed method exhibits the efficient performance on fingerprint verification compared with a commercial fingerprint matching software and the conventional method., Comment: IJCB2020

Biometric attendance system has become more popular over the conventional system due to its various advantages. Fingerprint verification system is one of the promising methods installed in various organizations for attendance purpose. Usage of image inpainting technique in fingerprint verification improves the performance of fingerprint verification system. This paper highlights on some image inpainting techniques useful in fingerprint verification system. This paper also focuses on motivation to use image inpainting in fingerprint verification system. The fingerprint verification system is proposed where image inpainting algorithm shall be used to improve the performance of fingerprint verification system.

A number of successful RF fingerprint classifiers have been demonstrated, but relatively few results evaluate the impact of changing the receiver used for training and inference. In this work, we record a set of 25 ZigBee transmitters with 10 independent, unsynchronized receivers and first show that similar performance may be achieved by a neural network-based RF fingerprint verification system on all receivers when training and inference are performed on the same receiver. Next, we show significant performance degradation when different receivers are used for training and inference. We propose two methods to address this shortcoming. The first method shows that the performance degradation is reduced when the training process uses several receivers. The second method proposes a calibration procedure whereby a single neural network-based transformation is learned per receiver and allows fingerprint verification models learned for a different receiver to be reused without retraining.