Fingerprint Authentication System Research Articles

Blind-Touch: Homomorphic Encryption-Based Distributed Neural Network Inference for Privacy-Preserving Fingerprint Authentication

Fingerprint authentication is a popular security mechanism for smartphones and laptops. However, its adoption in web and cloud environments has been limited due to privacy concerns over storing and processing biometric data on servers. This paper introduces Blind-Touch, a novel machine learning-based fingerprint authentication system leveraging homomorphic encryption to address these privacy concerns. Homomorphic encryption allows computations on encrypted data without decrypting. Thus, Blind-Touch can keep fingerprint data encrypted on the server while performing machine learning operations. Blind-Touch combines three strategies to efficiently utilize homomorphic encryption in machine learning: (1) It optimizes the feature vector for a distributed architecture, processing the first fully connected layer (FC-16) in plaintext on the client side and the subsequent layer (FC-1) post-encryption on the server, thereby minimizing encrypted computations; (2) It employs a homomorphic encryption-compatible data compression technique capable of handling 8,192 authentication results concurrently; and (3) It utilizes a clustered server architecture to simultaneously process authentication results, thereby enhancing scalability with increasing user numbers. Blind-Touch achieves high accuracy on two benchmark fingerprint datasets, with a 93.6% F1- score for the PolyU dataset and a 98.2% F1-score for the SOKOTO dataset. Moreover, Blind-Touch can match a fingerprint among 5,000 in about 0.65 seconds. With its privacy-focused design, high accuracy, and efficiency, Blind-Touch is a promising alternative to conventional fingerprint authentication for web and cloud applications.

MotoPrint: Reconfigurable Vibration Motor Fingerprint via Homologous Signals Learning

Device fingerprints can satisfy the high-security requirement of modern mobile applications (e.g., mobile payments) by guaranteeing the operation is performed on a trusted device. However, existing works on device fingerprints are weak to leakage, which leads to an irreversible failure of the device fingerprint authentication system after suffering from fingerprint theft attacks. The vulnerability drives us to propose a reconfigurable device fingerprint, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> , that can recover the system after suffering from such attacks. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> stems from the motor vibration that can represent in both signals of the accelerometer and the gyroscope (i.e., they are homologous motion signals). Therefore, we designed a two-path feature extracting network and a sensor-independent training strategy to eliminate sensor noise that can decline authentication performance. In addition, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> has a complete reconfiguration mechanism to cope with fingerprint leakage, which brings the damaged authentication system back to health. The evaluation of 80 stand-alone vibration motors and 20 in-built ones shows that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> can achieve high authentication accuracy of 98.5%. Meanwhile, we also demonstrate the reconfigured <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> , which can also effectively indicate the device's uniqueness with over 98% accuracy, is independent of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> s under other stimulating codes.

Intelligent and assisted medicine dispensing machine for elderly visual impaired people with deep neural network fingerprint authentication system

Offering a medication to Elderly and Visually Impaired People (EVIP) in rural areas without staff assistance is a difficult task in the field of health care. The current pill dispenser has limitations when it comes to administering medication before and after food, doesn't take into account over- and under-dosing situations, and doesn't send notifications to the relevant medical entities when the weight of the pills falls below the threshold. Despite the availability of numerous techniques and methods for automating the delivery of pills, there is no assistive method for the timely and secure delivery of medication. The suggested architecture provides EVIP with five crucial capabilities that aren't present in any of the current pill dispensers in an integrated way. In this paper, we present a biometric sensor-based secure access method for medication kits. The right person is only allowed access to the kit based on the finger print scanner. Second, the suggested architecture provides a self-managed and patient-centric service through assisted and automatic medication delivery. The third feature of this paper is the design method for the efficient delivery of pills before and after meals. A method of alerting the medical entities whenever a medication is over or under dosed is a fourth feature of the system. As a fifth feature, the implementation also automatically notifies the pharmacist and doctor when the weight of the pill strips in the chamber falls below the threshold value. Through the use of an integrated Timely Assistance Messaging (TAM) algorithm, all five features are implemented in the suggested architecture. A finger print recognition system is included with VGG-16 deep neural network with image enhancement. The EVIP can use this proposed system in residences, medical facilities, and retirement communities.

A novel verification framework to analyse security attacks in a fingerprint authentication system

The security vulnerabilities and flaws in the existing biometric fingerprint authentication system (FAS) are effectively overcome using the one time finger code (OTFC). Based on this OTFC technique, a new verification model is proposed in this work which is capable of evaluating and analysing the susceptibility of system to common security attacks. For verification and validation compliance, an integrated security framework with novel security protocol along with generation of a unique encryption key named terminal key is conceptualised and implemented. The exposure of the system to various security attacks are experimentally computed by measuring avalanche effect, which fetches an average value of 63.52%. Also, the severity of attack is verified by calculating base score obtained as 3, which is less in comparison with conventional systems. Hence it concludes that proposed FAS with new verification framework yields better results on exposure to common security threats than the conventional one.

A Secure Online Fingerprint Authentication System for Industrial IoT Devices over 5G Networks.

The development of 5G networks has rapidly increased the use of Industrial Internet of Things (IIoT) devices for control, monitoring, and processing purposes. Biometric-based user authentication can prevent unauthorized access to IIoT devices, thereby safeguarding data security during production. However, most biometric authentication systems in the IIoT have no template protection, thus risking raw biometric data stored as templates in central databases or IIoT devices. Moreover, traditional biometric authentication faces slow, limited database holding capacity and data transmission problems. To address these issues, in this paper we propose a secure online fingerprint authentication system for IIoT devices over 5G networks. The core of the proposed system is the design of a cancelable fingerprint template, which protects original minutia features and provides privacy and security guarantee for both entity users and the message content transmitted between IIoT devices and the cloud server via 5G networks.Compared with state-of-the-art methods, the proposed authentication system shows competitive performance on six public fingerprint databases, while saving computational costs and achieving fast online matching.

CAMERA BASED FINGERPRINT AUTHENTICATION SYSTEM USING MATLAB

— “At the end of the day, the goals are simple: safety and security” -Jodi Rell The above quote shows the importance of security in the whole society. We can see how safety and security is playing an important role in our day-to-day life. Despite the multitude of personal identification methods currently in practice, fingerprint verification has traditionally been considered one of the most reliable methods available. But is it the safest means during the time of COVID-19 pandemic? Our traditional scanner-based fingerprint authentication system requires the major upgrade to sustain in the current scenario. Can whole process be contactless…….

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.

Quantum dot Cellular Automata based Fault Tolerant Fingerprint Authentication Systems using Reversible Logic Gates

The limits and difficulties looked by CMOS innovation in the nano system has prompted the exploration of other potential advancements which can work with same functionalities anyway with lower power scattering and higher speed. One such technology is Quantum dot Cellular Automata (QCA). In this paper, QCA is explored to design the authentication system. This paper first presents the basic operating principle of a Fingerprint Authentication System (FAS) followed by fault tolerance analysis of four efficient XOR gate designs in the literature. The XOR gate is then used in the proposed four fault tolerant designs of reversible FAS in QCA, which are based on different reversible gates. Based on the evaluation of different performance parameters, it is seen that the proposed FAS designs are cost efficient and achieve improvement up to 59.46% in terms of number of cells, 67.16% improvement in cell area, 67.14% improvement in total area, 66.67% improvement in latency and 90.51% improvement in terms of circuit cost from the existing design Furthermore, the energy dissipation examination of the proposed designs is also additionally introduced. Subsequently, the proposed designs can be effectively used in biometric applications demanding ultra-low power consumption, higher operating speed and minimal area utilization.

Double Layered Security System for Smart ATM by Fingerprint and RF Technology

The crucial prerequisite in these days is to get rid of various forms of attacks. Nowadays, for financial transaction, automated teller machines (ATMs) are the mostly used gadgets in which personal identification numbers (PINs) are generally used for transaction. But personal identification numbers (PINs) are not secured from many types of threats (spoofing, eavesdropping, man-in-the middle attack etc.), which can affect the security of the confidential and private information. Due to this reason, different biometric systems gain popularity worldwide for their behavioral and physiological features. However, the current biometric systems, for example, iris, palm, faces or voice are extremely complex to use and have different disadvantages. In order to overcome these disadvantages a new concept has been introduced in this paper, for authentication in ATM a fingerprint authentication method and for information (finger print) transfer a combined approach fingerprint and RF technology scheme is used. Finger authentication system is implemented by the combination of fingerprint reader and fingerprint sensors. For the purpose of information (fingerprint) scanners work by capturing the pattern of ridges and valleys on a finger. The information is then scanners work by the device’s pattern analysis. For fingerprint authentication system, the experiment shows that in proposed classification the average recognition accuracy is 99.75% and 99.92 % and the execution time is 0.168 s and 0.187 s respectively..

Finger Print Based ATM System

Our Project is to develop the technique for fingerprint authentication in ATMs. This target can be mainly decomposed into image preprocessing, feature extraction and feature match. For each sub-task, some classical and up-to-date methods in literature are analyzed. Based on the analysis, an integrated solution for fingerprint recognition and authentication is developed for demonstration. Our demonstration program is coded in ASP.NET &amp; MATLAB. For the program, some optimization at coding level and algorithm level are proposed to improve the performance of my fingerprint authentication system. These performance enhancements are shown by experiments conducted upon a variety of fingerprint images. Also, the experiments illustrate the key issues of fingerprint recognition that are consistent with what the available literatures say.

Chaotic-Based Encryption Algorithm using Henon and Logistic Maps for Fingerprint Template Protection

Fingerprint is a reliable user authentication method as it is unique to individual users that makes it efficient for authenticating users. In a fingerprint authentication system, user fingerprint information is stored in databases in an image format known as a fingerprint template. Although fingerprint is reliable, the templates stored in the database are exposed to security threats either during the data transmission process over the network or in storage. Therefore, there is a need to protect the fingerprint template, especially in unsecured networks to maintain data privacy and confidentiality. Many past studies proposed fingerprint template protection (FTP) using chaotic-based encryption algorithms that are more suitable to secure images than conventional encryption such as DES, AES, and RSA. The chaotic-based encryption algorithms have been improved a lot in terms of their robustness. However, the robustness of the algorithm caused a trade-off to encryption speed where it remains an issue in FTP. Hence, this study aims to improve the limitations found in the existing chaotic-based encryption algorithms for FTP by improving its encryption speed using Henon and Logistic map. A series of simulations were conducted using MATLAB to evaluate the performance of the proposed chaotic-based encryption algorithm for FTP through different analyses covering key sensitivity, histogram, correlations, differential, information entropy, and encryption/decryption speed. The performance proposed encryption algorithm was promising which could be a starting point for detailed analysis and implementation in real application domains.

A Two-Stage Feature Transformation-Based Fingerprint Authentication System for Privacy Protection in IoT

The significant and rapid development of the Internet of Things (IoT) in recent years has greatly benefited people's lives. However, there are serious security and privacy concerns that need to be addressed when using the IoT for distributed authentication. In this article, we propose a secure fingerprint authentication system to protect user privacy for authentication on IoT devices. The proposed system applies a two-stage feature transformation scheme. Specifically, a weight-based fusion mechanism is designed in the first stage, while the second stage is featured by a linear convolution-based transformation with element removal from the convolution output to increase the security and protection. The proposed authentication system satisfies all the requirements of cancelable biometrics: accuracy, revocability, and diversity, unlinkability, and noninvertibility. Evaluated over six public fingerprint databases, the proposed authentication system exhibits highly competitive performance when compared with the existing cancelable fingerprint templates. Moreover, its energy efficiency on savings in memory space and low computational costs make the proposed scheme a good fit for resource-limited IoT devices.

A novel verification framework to analyse security attacks in a fingerprint authentication system

A novel verification framework to analyse security attacks in a fingerprint authentication system

A linear convolution-based cancelable fingerprint biometric authentication system

Authentication is a critical requirement of many systems, in domains such as law enforcement, financial services and consumer devices. Due to poor user practices, especially regarding passwords, biometric technologies have been presented as a viable solution, and have been constantly evolving to meet this requirement. It is important to consider the security aspects of any proposed biometric authentication system, as threats targeting biometric template data are serious. Given that the original biometric data are not revocable, if compromised, they are lost (or tainted) forever. To prevent biometric template data from being compromised by attackers, we propose a new linear convolution-based cancelable fingerprint authentication system. In the proposed system, instead of using the original feature data themselves as the inputs to the linear convolution function, the second input is replaced by a help vector, which guarantees that errors from one part of the template data do not impact other parts. Moreover, to ensure the safety of the help vector chosen from a help vector pool in the lost-key scenario, a feature-guided index generation algorithm is developed. The experimental results show that the proposed system achieves satisfactory recognition accuracy, while providing strong protection to fingerprint template data.

A Multi-Filter Fingerprint Matching Framework for Cancelable Template Design

Despite the ubiquity in the use of biometrics due to its many advantages against traditional methods such as password or token, the emerging cancelable biometric methods, which are designed to protect the biometrics are still exposed to certain threats. Attack via Record Multiplicity (ARM) is one of those. In this paper, we propose a novel framework that possesses two layers of authentication to improve the matching performance of a fingerprint authentication system in the cancelable template setting. In addition, a multi-filter fingerprint matching scheme is devised to deal more effectively with low-quality fingerprint images. Two techniques that are capable of defending against the heinous ARM are also introduced. Security analysis on the system's capability against the hill-climb attack and pre-image attack is also provided. The proposed scheme has been evaluated over public datasets FVC2002-DB1, FVC2002-DB2, FVC2002-DB3, and FVC2004-DB2. It has achieved the best result compared with the state-of-art methods. The source code for this framework is available on demand.

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.

Chaotic-Based Encryption Algorithm using Henon and Logistic Maps for Fingerprint Template Protection

Fingerprint is a reliable user authentication method as it is unique to individual users that makes it efficient for authenticating users. In a fingerprint authentication system, user fingerprint information is stored in databases in an image format known as a fingerprint template. Although fingerprint is reliable, the templates stored in the database are exposed to security threats either during the data transmission process over the network or in storage. Therefore, there is a need to protect the fingerprint template, especially in unsecured networks to maintain data privacy and confidentiality. Many past studies proposed fingerprint template protection (FTP) using chaotic-based encryption algorithms that are more suitable to secure images than conventional encryption such as DES, AES, and RSA. The chaotic-based encryption algorithms have been improved a lot in terms of their robustness. However, the robustness of the algorithm caused a trade-off to encryption speed where it remains an issue in FTP. Hence, this study aims to improve the limitations found in the existing chaotic-based encryption algorithms for FTP by improving its encryption speed using Henon and Logistic map. A series of simulations were conducted using MATLAB to evaluate the performance of the proposed chaotic-based encryption algorithm for FTP through different analyses covering key sensitivity, histogram, correlations, differential, information entropy, and encryption/decryption speed. The performance proposed encryption algorithm was promising which could be a starting point for detailed analysis and implementation in real application domains.

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.

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.

Portable and Efficient Fingerprint Authentication System Based on a Microcontroller

This paper presents the design of a fingerprint authentication system based on a simple microcontroller and the fingerprint sensor. The circuit diagram and details regarding the procedure are included. The system was programed in MPLAB and then embedded into the microcontroller. Communication between the PIC and sensor is by RS232 protocol. The results show that the system recognizes the fingerprint in less than 1 second. It is portable and there is no need for image processing. Furthermore, the system shows a high effectiveness when storing and verifying fingerprints.