Fingerprint-based Authentication Research Articles

FinTem: A secure and non-invertible technique for fingerprint template protection

In today’s automated society, authentication has become a necessity. Fingerprint-based authentication is the predominant choice due to its advantages. However, existing biometric methodologies have certain privacy and security challenges as they directly store the extracted minutiae points in the database, potentially enabling malicious entities to reconstruct fingerprints. The proposed approach constructs a Delaunay triangle from minutiae points, and features are extracted using triangle properties (incircle, circumcircle, and excircle). A secure user template is formed by incorporating fingerprint features, generating a secure bitstring, and utilizing a distinct user key achieved through the salting process. The system’s security is enhanced by feature mapping. The proposed method demonstrates exceptional revocability, diversity, security, and performance and has been rigorously evaluated on nine databases, namely FVC2000 (DB1 & DB2), FVC2002 (DB1, DB2, DB3, & DB4), FVC2004 (DB1 & DB2), and IIT Kanpur database, which is a large database.

Multiple Authentication Schemes for Sign-In Process

Abstract: In the ever-evolving landscape of digital applications, user authentication plays a vital role in ensuring data security and privacy. Traditional username-password authentication methods may not always suffice, leading to the need for "Alternate Sign-In Methods." This project aims to develop a robust and user-friendly authentication system using advanced Machine Learning (ML) techniques. The system's primary objective is to offer users multiple authentication options beyond the conventional methods, such as Image Authentication, Pattern-based Authentication, Username-Password Authentication, and Fingerprint-based Authentication. Content-based filtering and collaborative filtering approaches will be employed to ensure efficient and relevant authentication options for each user. The project focuses on enhancing user convenience, security, and overall experience in digital platforms by tailoring sign-in methods to individual preferences.

Fingerprint Based Driving License Management System

The "Fingerprint-Based Driving License Detector" is an innovative system designed to enhance the security and authenticity of driving licenses through the integration of biometric technology. This system leverages fingerprint recognition as a robust method to verify the identity of drivers, aiming to reduce instances of license fraud and unauthorized use. The proposed system consists of a comprehensive framework that involves capturing and storing the fingerprint data of individuals during the driving license issuance process. This biometric data is then securely linked to the respective driving license records in a centralized database. During routine traffic stops or license verification processes, law enforcement authorities can use fingerprint scanners to quickly and accurately verify the identity of the license holder. The implementation of this system offers several advantages, including heightened security, reduced instances of identity theft, and a more streamlined and efficient license verification process. Additionally, the integration of fingerprint-based authentication enhances the overall trustworthiness of the driving license system, fostering a safer and more reliable environment on the roads. Keywords: Fingerprint Recognition, Driving License Authentication, Biometric Technology, Identity Verification.

Fingerprint based authentication framework for IoT environment

<p>Internet of Things (IoT) approach makes the humans life easy and safe and due to its acceptability and portability. Humans are surrounded by multitude of heterogeneous devices which assist them in their daily routine. The integration of Blockchain technology with IoT played an important role in medical science and online transaction. At same point of time, the IoT network and devices are vulnerable that can be exploit to affect the network and devices security and accessibility. Therefore, robust authentication techniques should be implementing to secure the IoT network as well as devices accessibility. Due to high heterogeneity of applications, providing authentication for these devices is always challengeable. In this work, a novel fingerprint-based authentication framework in implemented in which Gaussian filter is used for image smoothness, Robert filter for edge detection and achieve 94% accuracy while authentication.</p>

Smartphone-based Biometric Authentication Scheme for Access Control Management in Client-server Environment

As the information put together by the blend of smartphones, the cloud, the IOT, and ubiquitous computing continue to expand at an alarming rate, a data breach increases. Today, users' strong authentication and authorization approaches are increasingly important to secure sensitive, confidential, secret information. Possession and knowledge-based authentication techniques for computers, the internet, email accounts, etc., are commonly used to access vital information; they do not link a user to an established identity, resulting in most security vulnerabilities. Biometric authentication, on the other hand, has the privilege of being more reliable than traditional authentication as biometric characteristics of a person can’t be lost; they are tough to distribute, exchange or duplicate; and it requires the user to be present during the authentication process, thereby relating the users to established identities. Biometrics provides a higher level of assurance that the individual attempting to ascertain is the individual in question. Thus, resulting in a more reliable, usable, and cost-effective model with a higher level of protection to deter an attacker from obtaining entry to a computer or network and gaining access to confidential data. This paper introduces a novel fingerprint-based authentication scheme for mobile environments, enabling user authentication based on fingerprint features using a challenge-response-based authentication process. In this study, the proposed authentication system has been developed on a real Android-based smartphone, tested on actual users, and performance analysis has been carried out; empirical results reveal that the proposed authentication scheme achieves increased performance. Moreover, a usability analysis has been done to determine efficiency, effectiveness, and user satisfaction. The evaluation results indicate its feasibility to use it as an effective authentication mechanism for mobile phone environments.

Secure bike start

This project is titled “Secure Bike Start” with Arduino. This project used to propose a system that describes the concept of controlling vehicle theft by using the method of fingerprint-based authentication and which alerts the user by providing the notification to the user. This proposes a system that identifies the authenticated person for the vehicle and starts up the vehicle when the person is approved by the FingerPrint Module. If any unauthorized access to the vehicle is to be applied then the system is fully locked and there is no way to move the vehicle from the place of parking. The capability to start-ups the vehicle is initiated by the Positive Charge from the Finger Print Module. Hence, the Ignition System control is based on authentication. The existing system provides security but there is no locking system. The proposed system describes the security of the vehicle as well as locking the whole Vehicle system by the way of an authentication basis.

A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms

Amongst all biometric-based personal authentication systems, a fingerprint that gives each person a unique identity is the most commonly used parameter for personal identification. In this paper, we present an automatic fingerprint-based authentication framework by means of fingerprint enhancement, feature extraction, and matching techniques. Initially, a variant of adaptive histogram equalization called CLAHE (contrast limited adaptive histogram equalization) along with a combination of FFT (fast Fourier transform), and Gabor filters are applied to enhance the contrast of fingerprint images. The fingerprint is then authenticated by picking a small amount of information from some local interest points called minutiae point features. These features are extracted from the thinned binary fingerprint image with a hybrid combination of Harris and SURF feature detectors to render significantly improved detection results. For fingerprint matching, the Euclidean distance between the corresponding Harris-SURF feature vectors of two feature points is used as a feature matching similarity measure of two fingerprint images. Moreover, an iterative algorithm called RANSAC (RANdom SAmple Consensus) is applied for fine matching and to automatically eliminate false matches and incorrect match points. Quantitative experimental results achieved on FVC2002 DB1 and FVC2000 DB1 public domain fingerprint databases demonstrate the good performance and feasibility of the proposed framework in terms of achieving average recognition rates of 95% and 92.5% for FVC2002 DB1 and FVC2000 DB1 databases, respectively.

A multilayer system to boost the robustness of fingerprint authentication against presentation attacks by fusion with heart-signal

Vulnerability to presentation attacks (PAs) remains one of the main security concerns of the widely used fingerprint-based authentication systems, especially for unattended and remote applications. PAs can be carried out by presenting artifact, corpse, or conformant samples to a biometric sensor with the intention of circumventing the system policy. In this study, we develop a multilayer biometric authentication system robust against PAs by the fusion of fingerprint and heart-signal. In the first layer, artifact attacks are prevented by using a fine-tuned convolutional neural network (CNN). In the second layer, a lightweight CNN is used for the prevention of corpse attacks by using heart-signal (also known as ECG signal) with duration of 0.5 s. In the subsequent layers, robust fingerprint matcher at a specific threshold are utilized for the prevention of conformant attacks. In the final layer, a score-level fusion of the fingerprint and heart-signal is used for biometric authentication. The proposed system was evaluated by different authentication and attack scenarios using a multimodal dataset comprising two public databases of fingerprints and heart-signals available online. The experimental results yielded a false match rate (FMR) of approximately zero (0.1%) with an acceptable false non-match rate (FNMR). The obtained results are encouraging for the incorporation of the system into applications requiring high-security authentication.

On the transferability of adversarial perturbation attacks against fingerprint based authentication systems

The growing availability of cheap and reliable fingerprint acquisition scanners is resulting in an increasing spread of Fingerprint-based Authentication Systems (FAS) in consumer electronics. This has giving rise to a new wave in research on both smarter spoofing attacks, aimed to bypass a FAS by using a counterfeit fingerprint, and on more effective Liveness Detectors (LD), aimed to discern authentic (live) fingerprints from fake ones. As in many other computer vision tasks, deep Convolutional Neural Networks (CNN) demonstrated to be very effective also for fingerprint liveness detection. However, we showed that it is possible to adapt adversarial perturbation approaches to mislead CNN-based LD. In this paper, we want to make a step further toward the design of a black-box attack by investigating whether it is possible to transfer a perturbation across different CNN liveness detectors in the case of a target LD very different from the one used to compute the perturbations. To this aim, we designed an attack scenario where a shadow LD (i.e. an adaptation of the substitute technique for the liveness detection application) is used to generate an adversarial fingerprint in a white-box setting before submitting it to the real target LD, invoked in a total back-box manner. Finally, we analysed the impact that such attack has on the authentication system, also analysing if and to what extent the scanner and the spoofing material combinations affect the success of the attack.

Optimized Authentication System with High Security and Privacy

Authentication and privacy play an important role in the present electronic world. Biometrics and especially fingerprint-based authentication are extremely useful for unlocking doors, mobile phones, etc. Fingerprint biometrics usually store the attributes of the minutia point of a fingerprint directly in the database as a user template. Existing research works have shown that from such insecure user templates, original fingerprints can be constructed. If the database gets compromised, the attacker may construct the fingerprint of a user, which is a serious security and privacy issue. Security of original fingerprints is therefore extremely important. Ali et al. have designed a system for secure fingerprint biometrics; however, their technique has various limitations and is not optimized. In this paper, first we have proposed a secure technique which is highly robust, optimized, and fast. Secondly, unlike most of the fingerprint biometrics apart from the minutiae point location and orientation, we have used the quality of minutiae points as well to construct an optimized template. Third, the template constructed is in 3D shell shape. We have rigorously evaluated the technique on nine different fingerprint databases. The obtained results from the experiments are highly promising and show the effectiveness of the technique.

LB and LT feature approach to personal identification using finger knuckle image biological trait

Biometric identification is an emerging field for personal authentication and has a large number of applications in the field of time attendance system and forensic domain. A variety of biometric traits are available, but among them, hand-based biometrics are more popular because of their ease of use and better performance. A lot of literature is available on fingerprint identification but it is observed that fingerprints are always not a reliable source of information to be captured from the crime scene to identify suspects. Therefore, it is required to use some other hand-based biological trait such as finger knuckle print (back side of finger joint skin pattern) in order to identify the suspect. This paper proposed a finger knuckle image-based person identification. The performance of the proposed biometric system is compared with the well-established fingerprint-based authentication system. The experiments were performed on the benchmark dataset like PolyU finger knuckle print dataset and FVC2002 fingerprint dataset. The experimental results show that the performance of the proposed algorithm leads over many fingerprint-based identification systems. The proposed algorithm can also be used to design finger knuckle image-based biometric systems instead of fingerprint-based biometric systems.

LB & LT Feature Approach to Personal Identification Using Finger Knuckle Image Biological Trait.

Biometric identification is an emerging field for personal authentication and has a large number of applications in the field of time attendance system and forensic domain. A variety of biometric traits are available, but among them, hand-based biometrics are more popular because of their ease of use and better performance. A lot of literature is available on fingerprint identification but it is observed that fingerprints are always not a reliable source of information to be captured from the crime scene to identify suspects. Therefore, it is required to use some other hand-based biological trait such as finger knuckle print (back side of finger joint skin pattern) in order to identify the suspect. This paper proposed a finger knuckle image-based person identification. The performance of the proposed biometric system is compared with the well-established fingerprint-based authentication system. The experiments were performed on the benchmark dataset like PolyU finger knuckle print dataset and FVC2002 fingerprint dataset. The experimental results show that the performance of the proposed algorithm leads over many fingerprint-based identification systems. The proposed algorithm can also be used to design finger knuckle image-based biometric systems instead of fingerprint-based biometric systems.

Methodological Analysis for Different Fingerprint Matching Techniques

Now a day’s fingerprint-based authentication system is becoming very popular because of its applications. The fingerprint of an individual is a unique and persistent feature that can be used for identification and authentication. There are several different methods that has been implemented by different researchers for the fingerprint recognition. In this paper, we examine writing of various systems and angles utilized for unique finger impression acknowledgment.

Efficient Privacy-Preserving Fingerprint-Based Authentication System Using Fully Homomorphic Encryption

To help smartphone users protect their phone, fingerprint-based authentication systems (e.g., Apple’s Touch ID) have increasingly become popular in smartphones. In web applications, however, fingerprint-based authentication is still rarely used. One of the most serious concerns is the lack of technology for securely storing fingerprint data used for authentication. Because scanned fingerprint data are not exactly the same each time, the use of a traditional cryptographic hash function (e.g., SHA-256) is infeasible to protect raw fingerprint data. In this paper, we present an efficient privacy-preserving fingerprint authentication system using a fully homomorphic encryption scheme in which fingerprint data are always stored and processed in an encrypted form. We implement a fully working fingerprint authentication system with a fingerprint database (containing 4,000 samples) using the Fast Fully Homomorphic Encryption over the Torus (TFHE) library. The proposed system can perform the fingerprint matching process within about 166 seconds (±0.564 seconds) on average.

Strengthen user authentication on mobile devices by using user\u2019s touch dynamics pattern

Mobile devices, particularly the touch screen mobile devices, are increasingly used to store and access private and sensitive data or services, and this has led to an increased demand for more secure and usable security services, one of which is user authentication. Currently, mobile device authentication services mainly use a knowledge-based method, e.g. a PIN-based authentication method, and, in some cases, a fingerprint-based authentication method is also supported. The knowledge-based method is vulnerable to impersonation attacks, while the fingerprint-based method can be unreliable sometimes. To overcome these limitations and to make the authentication service more secure and reliable for touch screen mobile device users, we have investigated the use of touch dynamics biometrics as a mobile device authentication solution by designing, implementing and evaluating a touch dynamics authentication method. This paper describes the design, implementation, and evaluation of this method, the acquisition of raw touch dynamics data, the use of the raw data to obtain touch dynamics features, and the training of the features to build an authentication model for user identity verification. The evaluation results show that by integrating the touch dynamics authentication method into the PIN-based authentication method, the protection levels against impersonation attacks is greatly enhanced. For example, if a PIN is compromised, the success rate of an impersonation attempt is drastically reduced from 100% (if only a 4-digit PIN is used) to 9.9% (if both the PIN and the touch dynamics are used).

Towards a fast and secure fingerprint authentication system based on a novel encoding scheme

Fingerprint-based authentication systems in general are prone to several security vulnerabilities. Authentication systems such as Biometric crypto systems, Cancellable templates and Bio-hashing provide a solution for addressing these vulnerabilities. But these systems are vulnerable to the unauthorized access resulting from the spoofed fingerprint templates by the fraudulent users. Hence, it is essential to enhance the features of the existing Biometric fingerprint-based authentication systems. An extensive research has been carried out by various researchers on the existing fingerprint authentication system techniques and it is found that none of them are fully capable of eliminating the security vulnerabilities. Fingerprint authentication system technique based on one time fingerprint template provides a solution for this by generating one time template from the fingerprint features. Although this method addresses vulnerability issues to a certain extent, improvements are highly essential particularly in terms of their security and performance. There are several systems based on finger code, where fingerprint features will be converted into finger code using a circular tessellation technique. Although authentication based on this technique improves security, the possibility of compromising the finger code is a huge threat, which needs to be addressed. In this paper, an innovative model is proposed which generates a secure one time finger code during every user authentication. It is generated using finger code obtained from minutiae vectors using a circular tessellation approach, pseudo-random number generators and timestamp, which are generated during every user transaction session. This unique encoding approach makes it extremely difficult for an unauthorized user to decode the generated finger code that is used for a particular authentication session. Thus, the possibility of compromise of original fingerprint can be avoided and thus security of biometric system can be enhanced. The proposed system also provides better performance in terms of its accuracy, processing speed and complexity.

QR Code and Biometric Based Authentication System for Trains

The utility of a QR Code, is plenty in terms of usage to identify a particular entity. They are barcode of sorts that can hold a certain data set. Biometric security measures are one of the safest security measures, with fingerprint based biometric security being a rather easy and efficient way. In this paper, we discuss a way to efficiently check-in to a seat, during train travel by using a QR Code and fingerprint-based authentication system. A Quick Response Code (QR Code) can be used to mark one’s attendance at their seat. Fingerprint authentication is the means to authenticate a person’s identity without having to carry any form of physical identity proof along with them, during travel. Given the present system to check tickets is rather redundant and time consuming, having to manually check the ticket and identity of each passenger. By using this system, we can create a more efficient and less time-consuming system, that has much lesser probabilities of human error to occur.

Fingerprint-Based Quantum Authentication Scheme Using Encoded Graph States

We demonstrate a novel fingerprint-based quantum authentication scheme with encoded graph states. This scheme is designed to solve the practical problem in knowledge-based quantum authentication systems and could make users get rid of remembering a large of number of passwords. What’s more, it could satisfy the requirement of secure remote communication by using fingerprint-encoded graph states, which would solve the security and privacy problems existing in the traditional fingerprint identification system. Security analysis shows that the proposed scheme could effectively defend various attacks including forgery attack, intercept-resend attack and man-in-the-middle attack. And, this scheme takes advantages of the merits in terms of both fingerprint recognition and quantum authentication, rendering it more secure, convenient and practical for users than its original counterpart, knowledge-based quantum authentication.

MasterPrint: Exploring the Vulnerability of Partial Fingerprint-Based Authentication Systems

This paper investigates the security of partial fingerprint-based authentication systems, especially when multiple fingerprints of a user are enrolled. A number of consumer electronic devices, such as smartphones, are beginning to incorporate fingerprint sensors for user authentication. The sensors embedded in these devices are generally small and the resulting images are, therefore, limited in size. To compensate for the limited size, these devices often acquire multiple partial impressions of a single finger during enrollment to ensure that at least one of them will successfully match with the image obtained from the user during authentication. Furthermore, in some cases, the user is allowed to enroll multiple fingers, and the impressions pertaining to multiple partial fingers are associated with the same identity (i.e., one user). A user is said to be successfully authenticated if the partial fingerprint obtained during authentication matches any one of the stored templates. This paper investigates the possibility of generating a “MasterPrint,” a synthetic or real partial fingerprint that serendipitously matches one or more of the stored templates for a significant number of users. Our preliminary results on an optical fingerprint data set and a capacitive fingerprint data set indicate that it is indeed possible to locate or generate partial fingerprints that can be used to impersonate a large number of users. In this regard, we expose a potential vulnerability of partial fingerprint-based authentication systems, especially when multiple impressions are enrolled per finger.

Fingerprint Liveness Detection From Single Image Using Low-Level Features and Shape Analysis

Fingerprint-based authentication systems have developed rapidly in the recent years. However, current fingerprint-based biometric systems are vulnerable to spoofing attacks. Moreover, single feature-based static approach does not perform equally over different fingerprint sensors and spoofing materials. In this paper, we propose a static software approach. We propose to combine low-level gradient features from speeded-up robust features, pyramid extension of the histograms of oriented gradient and texture features from Gabor wavelet using dynamic score level integration. We extract these features from a single fingerprint image to overcome the issues faced in dynamic software approaches, which require user cooperation and longer computational time. A experimental analysis done on LivDet 2011 data produced an average equal error rate (EER) of 3.95% over four databases. The result outperforms the existing best average EER of 9.625%. We also performed experiments with LivDet 2013 database and achieved an average classification error rate of 2.27% in comparison with 12.87% obtained by the LivDet 2013 competition winner.