Fingerprint Authentication Systems Research Articles

Developing and valuation for Techniques Fingerprint Recognition System

The distinctive patterns observed on human fingertips are a result of the combination of ridges and troughs present on the skin surface. These patterns achieve their complete development during the period of pregnancy and remain stable throughout an individual's lifespan. Fingerprints refer to the impressions formed by the unique patterns present on the fingertips. Bruising resulting from accidental injuries, such as cuts and burns, can temporarily compromise the visibility of fingerprints. However, after the lesion has fully healed, the original patterns will be restored. Fingerprint recognition is a highly dynamic and extensively researched domain within the realm of biometrics. The word refers to the abbreviated form of the computational technique employed to ascertain the concordance between two distinct human fingerprints. In this naturally tough pattern recognition issue, it is imperative to minimize two error rates: the False Accept Rate (FAR) and the False Reject Rate (FRR). These mistake rates are in competition with each other. The development of computing capabilities has facilitated the creation of Automated Fingerprint Authentication Systems (AFIS), leading to a substantial increase in research activity, particularly in the past twenty years. This article aims to provide a comprehensive review of the issue surrounding fingerprint recognition, including an analysis of its underlying design and implementation difficulties, along with an assessment of its potential future developments.

Adversarial liveness detector: Leveraging adversarial perturbations in fingerprint liveness detection

AbstractThe widespread use of fingerprint authentication systems (FASs) in consumer electronics opens for the development of advanced presentation attacks, that is, procedures designed to bypass a FAS using a forged fingerprint. As a consequence, FAS are often equipped with a fingerprint presentation attack detection (FPAD) module, to recognise live fingerprints from fake replicas. In this work, a novel FPAD approach based on Convolutional Neural Networks (CNNs) and on an ad hoc adversarial data augmentation strategy designed to iteratively increase the considered detector robustness is proposed. In particular, the concept of adversarial fingerprint, that is, fake fingerprints disguised by using ad hoc fingerprint adversarial perturbation algorithms was leveraged to help the detector focus only on salient portions of the fingerprints. The procedure can be adapted to different CNNs, adversarial fingerprint algorithms and fingerprint scanners, making the proposed approach versatile and easily customisable todifferent working scenarios. To test the effectiveness of the proposed approach, the authors took part in the LivDet 2021 competition, an international challenge gathering experts to compete on fingerprint liveness detection under different scanners and fake replica generation approach, achieving first place out of 23 participants in the ‘Liveness Detection in Action track’.

An Effective Orchestration for Fingerprint Presentation Attack Detection

Fingerprint presentation attack detection has become significant due to a wide-spread usage of fingerprint authentication systems. Well-replicated fingerprints easily spoof the authentication systems because their captured images do not differ from those of genuine fingerprints in general. While a number of techniques have focused on fingerprint presentation attack detection, they suffer from inaccuracy in determining the liveness of fingerprints and performance degradation on unknown types of fingerprints. To address existing limitations, we present a robust fingerprint presentation attack detection method that orchestrates different types of neural networks by incorporating a triangular normalization method. Our method has been evaluated on a public benchmark comprising 13,000 images with five different fake materials. The evaluation exhibited our method’s higher accuracy in determining the liveness of fingerprints as well as better generalization performance on different types of fingerprints compared to existing techniques.

An IoT-Oriented Privacy-Preserving Fingerprint Authentication System

Identity authentication has become an essential component for access control in the Internet of Things (IoT) environment. To overcome the inherent weakness of password-based authentication, many present IoT devices (e.g., commercial banking smart cards) are equipped with the fingerprint authentication mechanism. However, due to the resource constraints of IoT devices, oversimplified authentication schemes are deployed, which compromise system performance significantly. Moreover, fingerprint templates in these existing schemes are unprotected. To address these issues, we propose an IoT-oriented privacy-preserving fingerprint authentication system. The proposed system is composed of four main components: 1) minutiae extraction; 2) the minutia cylinder-code (MCC)-based cancelable binary template, generated by the proposed normalized random projection; 3) the lightweight, privacy-preserving template, built by novel pairwise Boolean operations; and 4) fingerprint matching. Our system can effectively mitigate preimage and hill-climbing attacks. A prototype of the proposed system is developed using a popular open-source platform (i.e., Open Virtual Platforms). Comprehensive experimental results on eight benchmark data sets validate the effectiveness of the proposed IoT-oriented fingerprint authentication system. Our system also achieves equivalent authentication accuracy to that of the unprotected fingerprint authentication systems deployed in the resource-rich, non-IoT environment. More importantly, our system prototype is deployable to commercially available low-cost smart cards, such as Atmel AT24C256C Memory Smart Card 256K Bits. To the best of our knowledge, the proposed system is the first privacy-preserving, cancelable fingerprint authentication system developed in such a resource-constrained IoT setting.

On the methodology of fingerprint template protection schemes conception : meditations on the reliability

Among the most major potential attacks against fingerprint authentication systems are those that target the stored reference templates. These threats are extremely damaging as they can lead to the invasion of user privacy. The countermeasures to secure fingerprint templates are therefore an indisputable necessity. In literature, although there are so many approaches that address this kind of vulnerability, it turns out to be very difficult to generalize their uses. Given that each system has its own particularities, going from the fingerprint trait acquisition to the matching process, the majority of protection schemes, that are proposed as generic solutions, are not sufficiently mature for large-scale deployment. Consequently, we believe that the methodology of fingerprint template protection schemes conception should be oriented to build specific protection schemes for every unprotected system, which will provide the best compromise between performance and security compared to any generic protection solution. By adopting this methodology, we propose in this paper a new protection scheme for fingerprint templates that is well adapted to a well-known existing unprotected fingerprint minutia system. Our experimental results, obtained using standard benchmarks such as FVC 2002 DB1 and DB2, have proven that the proposed technique meets the requirements of revocability, unlinkability, non-invertibility, and high recognition accuracy.

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.

Wireless Sensor Network Physical Layer Authentication Technology Based on Dynamic Prediction of Electromagnetic Fingerprints

The physical layer characteristics of the signal, such as electromagnetic fingerprints, will change with the time-varying channel’s variation under the influence of various factors, so that the reliability of traditional electromagnetic fingerprint authentication systems that don’t consider the dynamic changes of electromagnetic fingerprints would be reduced. In this paper, we explore and design a wireless sensor network physical layer authentication mechanism based on electromagnetic fingerprint dynamic prediction to improve the reliability of the authentication system. This system uses the LSTM network to learn and predict the dynamic of electromagnetic fingerprints affected by wireless channels and combines the unique electromagnetic fingerprint differences of legal transmitters with the channel corresponding changes to transceivers to improve the difference between the electromagnetic fingerprints from legitimate communication and the attack link, further improving the accuracy of electromagnetic fingerprint authentication. In addition, the LSTM network can also find the abnormal data and learn through continuous adaptive training to improve the detection probability.

A Digital Geometry-Based Fingerprint Matching Technique

Fingerprint authentication systems have become ubiquitous with the event of smart devices and cheap sensors. Because of its use in a wide range of devices, it is important that the biometric authentication systems are computationally cheap, and at the same time robust to distortions and noises. Most of the existing fingerprint authentication systems are based on conventional mathematics or complex filter and consequently features extracted are computationally expensive or gross approximate with a large margin of errors leading to drops in the accuracy of the system. In this paper, we propose a fingerprint template based on translation and rotation invariant features of the delaunay triangulation of minutiae. However existing methods of comparison of delaunay triangles features do not always result in an optimum match because of the distortion in the different prints of the same finger. In the proposed method, features extracted from the delaunay triangulation are augmented with the information of local ridges based on discrete curvature and digital straightness. Experimentation with FVC2000 shows that the proposed method has better performances compared to the latest techniques.

A novel fingerprint template protection and fingerprint authentication scheme using visual secret sharing and super-resolution

Fingerprint is the most recommended and extensively practicing biometric trait for personal authentication. Most of the fingerprint authentication systems trust minutiae as the characteristic for authentication. These characteristics are preserved as fingerprint templates in the database. However, it is observed that the databases are not secure and can be negotiated. Recent studies reveal that, if a person’s minutiae points are dripped, fingerprint can be restored from these points. Similarly, if the fingerprint records are lost, it is a permanent damage. There is no mechanism to replace the fingerprint as it is part of the human body. Hence there is a necessity to secure the fingerprint template in the database. In this paper, we introduce a novel fingerprint template protection and fingerprint authentication scheme using visual secret sharing and super-resolution. During enrollment, a secret fingerprint image is encrypted into n shares. Each share is stored in a distinct database. During authentication, the shares are collected from various databases. The original secret fingerprint image is restored using a multiple image super-resolution procedure. The experimental results show that the reconstructed fingerprints are similar to the original fingerprints. The proposed method is robust, secure, and efficient in terms of fingerprint template protection and authentication.

LOPA: A linear offset based poisoning attack method against adaptive fingerprint authentication system

Biological characteristics have been widely used in various identity authentication systems. The authentication systems typically store one or several biometric templates to identify whether a claimed user is legitimate. However, since the biological characteristics of users may undergo intra-class variabilities (such as aging or injuring by accidents) as time goes by, those initial enrolled templates may be not able to match the latest characteristics of the users. Therefore, some adaptive systems have been proposed to continuously update the enrolled templates by using collected run-time data. However, a smart attacker can leverage this self-updating procedure to drift the stored templates by constructing and submitting a set of well-designed poisoning samples. In this paper, for the first time, we propose a novel linear offset based poisoning attack method, named “LOPA”, against the online self-update fingerprint authentication systems. By making minor linear transformation to the minutia representation matrix of a victim’s fingerprint template, the proposed attack method can generate a series of poisoning samples which are then submitted to the fingerprint authentication system. In this way, the initial template stored in the system will be imperceptibly and stealthily poisoned (i.e., updated), and eventually becomes unusable. Experimental results show that the proposed LOPA method is effective, where the stored fingerprint templates have been successfully poisoned, and those target fingers are incorrectly denied by the target system after a certain time. Specifically, the performance (the average GAR of all target fingers) of the fingerprint authentication system has dropped by 42.86%. In addition, the average matching score and the average matched minutia pairs of all target fingers have both declined, which indicate the universality of the proposed poisoning attack. This work reveals a novel security threat to the fingerprint authentication systems, and can hopefully provide references for developing future countermeasures.

A Novel Weber Local Binary Descriptor for Fingerprint Liveness Detection

In recent years, fingerprint authentication systems have been extensively deployed in various applications, including attendance systems, authentications on smartphones, mobile payment authorizations, as well as various safety certifications. However, similar to the other biometric identification technologies, fingerprint recognition is vulnerable to artificial replicas made from cheap materials, such as silicon, gelatin, etc. Thus, it is especially necessary to distinguish whether a given fingerprint is a live or a spoof one prior to such authentication. In order to solve the problems above, a novel local descriptor named Weber local binary descriptor for fingerprint liveness detection (FLD) has been proposed in this paper. The method consists of two components: the local binary differential excitation component that extracts intensity-variance features and the local binary gradient orientation component that extracts orientation features. The co-occurrence probability of the two components is calculated to construct a discriminative feature vector, which is fed into support vector machine (SVM) classifiers. The effectiveness of the proposed method is intuitively analyzed on the image samples and numerically demonstrated by Mahalanobis distance. Experiments are performed on two public databases from FLD competitions from 2011 and 2013. The results have proved that the proposed method obtains the best detection accuracy among the existing image local descriptors in FLD.

Adversarial attacks on fingerprint liveness detection

Deep neural networks are vulnerable to adversarial samples, posing potential threats to the applications deployed with deep learning models in practical conditions. A typical example is the fingerprint liveness detection module in fingerprint authentication systems. Inspired by great progress of deep learning, deep networks-based fingerprint liveness detection algorithms spring up and dominate the field. Thus, we investigate the feasibility of deceiving state-of-the-art deep networks-based fingerprint liveness detection schemes by leveraging this property in this paper. Extensive evaluations are made with three existing adversarial methods: FGSM, MI-FGSM, and Deepfool. We also proposed an adversarial attack method that enhances the robustness of adversarial fingerprint images to various transformations like rotations and flip. We demonstrate these outstanding schemes are likely to classify fake fingerprints as live fingerprints by adding tiny perturbations, even without internal details of their used model. The experimental results reveal a big loophole and threats for these schemes from a view of security, and enough attention is urgently needed to be paid on anti-adversarial not only in fingerprint liveness detection but also in all deep learning applications.

Semi-supervised stacked autoencoder-based deep hierarchical semantic feature for real-time fingerprint liveness detection

The popularity of biometric authentication technology benefits from the rapid development of smart mobile devices in recent years, and fingerprints, which are inherent human traits and neither easily revealed nor deciphered, can be used for real-time individual authentication systems. However, the main security issue of real-time fingerprint authentication systems is that most fingerprint scanners are vulnerable to presentation attacks by artificial replicas, made from plastic clay, gelatin, silicon, wood glue, etc. One anti-spoofing attack scheme, called real-time fingerprint liveness detection (RFLD), has been proposed to discriminate live or fake fingerprints. Currently, to resolve the presentation attacks, most RFLD solutions all relied on handcrafted feature extraction and selection. The features extracted by manual method are shallow features of the samples; however, autoencoder can automatically learn deep hierarchical semantic features representation of the samples, thus replacing the operations extracted with hand-designed features. In this paper, we apply stacked autoencoder to RFLD to significantly lower the work-force burden of the feature extraction engineering, and our model consists of two parts: parameter pre-training based on unsupervised learning and FLD based on supervised learning. The performance has been verified on two public fingerprint datasets: LivDet 2011 and 2013, and the experimental results indicate that our proposed approach works well for RFLD as well as the detection performance is satisfactory.

Fingerprint Liveness Detection Using an Improved CNN With Image Scale Equalization

Due to the lack of pre-judgment of fingerprints, fingerprint authentication systems are frequently vulnerable to artificial replicas. Anonymous people can impersonate authorized users to complete various authentication operations, thereby disrupting the order of life and causing tremendous economic losses to society. Therefore, to ensure that authorized users' fingerprint information is not used illegally, one possible anti-spoofing technique, called fingerprint liveness detection (FLD), has been exploited. Compared with the hand-crafted feature methods, the deep convolutional neural network (DCNN) can automatically learn the high-level semantic detail via supervised learning algorithm without any professional background knowledge. However, one disadvantage of most CNNs models is that fixed scale images (e.g., 227 × 227 ) are essential in the input layer. Although the scale problem can be handled by cropping or scaling operations via transforming an image of any scale into a fixed scale, they can easily cause some key texture information loss and image resolution degradation, which will weaken the generalization performance of the classifier model. In this paper, a novel FLD method called an improved DCNN with image scale equalization, has been proposed to preserve texture information and maintain image resolution. Besides, an adaptive learning rate method has been used in this paper. In the performance evaluation, the confusion matrix is applied into FLD for the first time as a performance indicator. The amounts of the experimental results based on the LivDet 2011 and LivDet 2013 data sets also verify that the detection performance of our method is superior to other methods.

Robust technique for fingerprint template protection

Fingerprint authentication systems generally save data extracted from fingerprint as minutiae template in the database. However, it is often found that databases can be attacked and compromised by the adversary. So if minutiae points of a user are leaked, the fingerprint can be generated from them. The fingerprint cannot be changed as the finger is a part of the human body. Hence, securing information extracted from the fingerprint is required. In this study, the authors propose a highly secure technique that uses location information of the minutia points to construct a highly secured template for a user. For every minutia point, secured modified location is generated by using information of its neighbouring minutiae and a key-set. They have achieved 2, 1, and 3.1% equal error rate values for FVC2002 DB1, DB2, and DB3 fingerprint databases, respectively, under the same key scenario. Analysis done for several attacks shows that the proposed technique is very robust and secure. The experimental results are extremely encouraging and they demonstrate the efficiency of the proposed technique.

3-Dimensional Secured Fingerprint Shell

Biometric systems relying on fingerprint usually use minutiae points information and store it directly in the database. However, databases are prone to get attacked by an adversary. As fingerprint can be generated using the information of its minutiae points, it is essential to ensure the security of the fingerprint data in biometric systems. Another problem associated with fingerprint authentication systems is the variation among fingerprint images of the same subject, which occurs due to rotation and translation of finger at the time of capturing fingerprints through sensors. A technique called Fingerprint Shell is proposed by Moujahdi et al. as a secured representation of fingerprint template. However, this technique uses only one intra-subject invariant feature, which is the distances between minutiae points and singular point to generate a 2-dimensional spiral curve which is used as a secured user template. In this paper, we analyze the weaknesses of the Fingerprint Shell and propose a highly secured fingerprint template protection technique in which a 3-dimensional spiral curve is generated as a secured user template for a fingerprint. To obtain the proposed secured fingerprint template, we utilize three intra-subject invariant features, namely distances between minutiae points and singular point, the orientation information of minutiae points and the ridge counts between minutiae points and singular point. Outcomes of the experimental analysis conducted on FVC (2000, 2002 and 2004) and IIT Kanpur databases show highly encouraging performance and exhibit the viability of the proposed technique. The technique has also been analyzed with respect to various attacks and is found to be highly secure and robust.

지문 영상 복원 기반의 스마트 기기 지문 스머지 공격 연구

지문 인증은 개인의 고유한 지문 특징 정보를 바탕으로 사용자를 식별하기 때문에 유출이나 분실, 망각의 위험이 없으며 편리하고 안전하여 여러 스마트 기기에 적용되고 있다. 하지만 스마트 기기를 사용한 후 남아있는 지문의 흔적은 스머지 공격에 취약하다. 본 논문에서는 사용자의 스마트 기기 사용 패턴을 분석하고 스마트 기기에 남아있는 지문 스머지를 이용하여 손상된 지문 영상을 복원하는 기법을 제안한다. 스마트 기기 사용 시 빈번하게 보이는 행동 패턴으로 구성된 시나리오에서 지문 스머지를 수집 및 복원하였으며 복원 지문 영상을 이용한 스머지 공격의 유효성에 대하여 실험하였다. 실험 결과, 지문 인증 시스템에서 높은 공격 성공률을 보였으며 스머지 공격에 취약함을 실증적으로 검증하였다. Fingerprint authentication identifies individuals based on user specific information. It is widely used as it is convenient, secure and has no risk of leakage, loss, or forgotten. However, the latent fingerprints remaining on the smart device's surface are vulnerable to smudge attacks. We analyze the usage patterns of individuals using smart device and propose methods to reconstruct damaged fingerprint images using fingerprint smudges. We examine the feasibility of smudge attacks with frequent usage situations by reconstructing fingerprint smudges collected from touch screens. Finally, we empirically verify the vulnerability of fingerprint authentication systems by showing high attack rates.

Fingerprint Liveness Detection Using Difference Co-occurrence Matrix Based Texture Features

Fingerprint authentication systems have been widely deployed in both civilian and government applications, however, whether fingerprint authentication systems is security or not has been an important issue under fraudulent attempts through artificial spoof fingerprints. In this paper, inspired by popular feature descriptors such as gray level co-occurrence matrix (GLCM) and Gradient (difference matrix (DM)), we propose a novel software-based fingerprint liveness detection algorithm called difference co-occurrence matrix (DCM). In doing so, quantization operation is firstly conducted on the images. DMs are constructed by calculating difference matrices of horizontal and vertical pixel values of images; difference co-occurrence arrays are constructed from the difference matrices between adjacent pixels. To reduce the influence of abnormal pixel values, truncation is used for DMs. Then, we compute four parameters (Angular Second Moment, Entropy, Inverse Differential Moment and Correlation) used as feature vectors of fingerprint images. For the first time in the fingerprint liveness detection, we construct eight difference co-occurrence matrices and extract texture features from processed DCMs. Finally, SVM classifier is used to predict classification accuracy. The experimental results reveal that our proposed method can achieve more accurate classification compared with the best algorithms of 2013 Fingerprint Liveness Detection Competition, while being able to recognize spoofed fingerprints with a better degree of accuracy.

Fingerprint Authentication Schemes for Mobile Devices

In certain applications, fingerprint authentication systems require templates to be stored in databases. The possible leakage of these fingerprint templates can lead to serious security and privacy threats. Therefore, with the frequent use of fingerprint authentication on mobile devices, it has become increasingly important to keep fingerprint data safe. Due to rapid developments in optical equipment, biometric systems are now able to gain the same biometric images repeatedly, so strong features can be selected with precision. Strong features refer to high-quality features which can be easily distinguished from other features in biometric raw images. In this paper, we introduce an algorithm that identifies these strong features with certain probability from a given fingerprint image. Once values are extracted from these features, they are used as the authentication data. Using the geometric information of these strong features, a cancelable fingerprint template can be produced, and the process of extracting values and geometric information is further explained. Because this is a light-weight authentication scheme, this template has practical usage for low performance mobile devices. Finally, we demonstrate that our proposed schemes are secure and that the user’s biometric raw data of the fingerprint are safe, even when the mobile device is lost or stolen.

A Delaunay Quadrangle-Based Fingerprint Authentication System With Template Protection Using Topology Code for Local Registration and Security Enhancement

Although some nice properties of the Delaunay triangle-based structure have been exploited in many fingerprint authentication systems and satisfactory outcomes have been reported, most of these systems operate without template protection. In addition, the feature sets and similarity measures utilized in these systems are not suitable for existing template protection techniques. Moreover, local structural change caused by nonlinear distortion is often not considered adequately in these systems. In this paper, we propose a Delaunay quadrangle-based fingerprint authentication system to deal with nonlinear distortion-induced local structural change that the Delaunay triangle-based structure suffers. Fixed-length and alignment-free feature vectors extracted from Delaunay quadrangles are less sensitive to nonlinear distortion and more discriminative than those from Delaunay triangles and can be applied to existing template protection directly. Furthermore, we propose to construct a unique topology code from each Delaunay quadrangle. Not only can this unique topology code help to carry out accurate local registration under distortion, but it also enhances the security of template data. Experimental results on public databases and security analysis show that the Delaunay quadrangle-based system with topology code can achieve better performance and higher security level than the Delaunay triangle-based system, the Delaunay quadrangle-based system without topology code, and some other similar systems.