Fingerprint Verification Research Articles

Fingerphoto morphing attack generation using texture descriptors based landmarks

Smartphone-based biometric authentication has been widely used in various applications. Among several biometric characteristics, fingerphoto biometrics captured from smartphones are gaining popularity owing to their usability, scalability across different smartphones, and reliable verification. However, fingerphoto verification systems are vulnerable to both direct and indirect attacks. In this work, we propose a novel method to generate morphing attacks on fingerphoto biometrics captured using smartphones. We introduce three different image-level fingerphoto morphing attack generation algorithms that can generate high-quality fingerphoto morphing images with minimum distortions. Extensive experiments were conducted on two datasets captured using different smartphones under various environmental conditions. The results demonstrate that the proposed morphing algorithms are highly vulnerable to commercial off-the-shelf and block-directional fingerprint verification systems. To effectively detect morphing attacks on fingerphoto biometrics, we propose the use of fingerphoto morphing attack detection algorithms that utilize both handcrafted and deep features. However, our detection results showed a high error rate in accurately detecting these types of attacks.

A deep learning-driven fingerprint verification model for enhancing exam integrity in Moroccan higher education

ABSTRACT In Moroccan higher education, the integrity of examinations is paramount, yet it faces the persistent challenge of identity impersonation. This form of academic dishonesty not only undermines the credibility of educational institutions but also contributes to the graduation of incompetent students. In an era where artificial intelligence is revolutionizing various sectors, its application to upholding academic integrity is both timely and essential. This paper proposes an advanced deep learning-driven fingerprint verification model specifically designed to combat impersonation in university examinations. Unlike traditional methods, our model leverages the power of Siamese neural networks (SNN), renowned for their effectiveness in learning distinct features and similarities. The model, trained, validated, and tested using the SOCOFing dataset, demonstrated high accuracy and effectiveness in fingerprint identification, crucial for verifying identities in educational exam settings. It achieved an accuracy of 99.29%, and an F1 score of 99.27%, surpassing other systems and significantly contributing to examination integrity in Moroccan higher education.

Development of a Face Recognition System for Authentication in Computer-Based Examinations: A Comparison with Fingerprint Verification

This study aims to overcome the limitations of fingerprint biometric authentication in computer-based examinations by introducing a robust face recognition system. The research targets key challenges such as administrative overhead, prolonged authentication times, and issues with false positives and negatives. By leveraging advanced technologies like Multi-Task Cascaded Convolutional Neural Network (MTCNN) for precise facial detection and FaceNet for efficient facial feature embedding, the developed system achieves substantial improvements across critical metrics. The evaluation metrics consistently report high ratings: accuracy 90%, speed 92%, ease of use 86%, robustness to variations 94%, security 92%, and privacy 90%. Evaluation of the system shows its superiority over traditional fingerprint methods. It demonstrates notably higher accuracy, efficiency, user-friendliness, adaptability to diverse conditions, and enhanced levels of security and privacy. These advancements position the face recognition system as a compelling alternative for enhancing the authentication processes in computer-based examinations. Keywords: FaceNet, Multi-Task Cascaded Convolutional Neural Network, Face Recognition, Security, Face, Fingerprint Detection. Sulaimon O.O., Olabiyisi, S.O. & Ismaila, W.O. (2024): Development of a Face Recognition System for Authentication in Computer-Based Examinations: A Comparison with Fingerprint Verification. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 2. Pp 117-128. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N2P8

A novel robust black-box fingerprinting scheme for deep classification neural networks

With the growing value and high training cost of deep neural network (DNN) models, there has been increasing research interest in deep neural network model watermarking. However, the existing literature lacks robust model watermarking methods with retrospective functionality, which hinders multi-user distribution and fine-grained copyright verification of deep neural network models. To achieve the model watermarking with retrospective functionality that is generally denoted the model fingerprinting, this study proposes a novel robust black-box fingerprinting scheme for deep classification neural networks, in which a fingerprint that is unique for each user is inserted in the given DNN. Specifically, a DWT-DCT-SVD-based embedding method is introduced to construct a high-quality poisoned image by spreading the fingerprint as a poisoned trigger to the entire image, which both leads to the traceability of the to-be-protected DNN and significantly enhances the stealthiness and anti-forgery of the embedded poisoned trigger. As the amplitude of the embedded fingerprint is rather small, it poses a challenge that makes the model hard to capture features of the embedded fingerprint during the training process. To tackle this challenge, this study then proposes a poisoned feature enhancement module, which groups classification categories of clean images without the fingerprint as one type while taking the poisoned images with the correct fingerprint and the poisoned adversarial images containing the other incorrect fingerprints as another two different types. This module, which can be removed during fingerprint verification, improves the reliability and fidelity of the model fingerprinting. Accordingly, an adversarial training strategy is designed, aiming at enhancing the effective training of classification neural network models that contain the fingerprint with small embedding strength. Combining these strategies then gives rise to the proposed scheme, which is denoted the UfNet for notational convenience. Extensive experimental simulation results demonstrate the excellent invisibility of the fingerprint in the poisoned image and the desirable verification of the fingerprint in case of even a one-bit difference. Additionally, it also exhibits robustness against the related attacks such as STRIP, Neural Cleanse, Fine-Pruning, and GradCAM, which outperforms the state-of-the-arts (SOTAs). Furthermore, the proposed UfNet is robust to collusion attacks and frame the innocent. These results show the feasibility and effectiveness of the introduced model fingerprinting framework and the proposed implementation methodology in this paper.

Improving the Security System for the Vehicle by Using the Driving License and Fingerprint Automation

In contemporary times, the widespread use of vehicles necessitates robust protection against theft, which is achieved through the integration of RFID tags and fingerprint authentication. This paper introduces a vehicle ignition system that leverages RFID tags and fingerprint recognition to safeguard vehicles from unauthorized access. Authorized RFID tags initiate the authentication process, complemented by fingerprint verification for driver identification. Upon successful authentication, a relay is activated, enabling the ignition system, while a red LED displays the status. In the event of authentication failure, an alarm buzzer prevents unauthorized entry. This system significantly enhances vehicle security and ensures a smooth user experience, with potential for customization and scalability across various vehicle models. Key components include In contemporary times, the widespread use of vehicles necessitates robust protection against theft, which is achieved through the integration of RFID tags and fingerprint authentication. This paper introduces a vehicle ignition system that leverages RFID tags and fingerprint recognition to safeguard vehicles from unauthorized access. Authorized RFID tags initiate the authentication process, complemented by fingerprint verification for driver identification. Upon successful authentication, a relay is activated, enabling the ignition system, while a red LED displays the status. In the event of authentication failure, an alarm buzzer prevents unauthorized entry. This system significantly enhances vehicle security and ensures a smooth user experience, with potential for customization and scalability across various vehicle models. Key components include Arduino, Fingerprint Sensor, RFID Reader, RFID Tag, GSM module, Relay, Motor, LED, and LCD. Key Words: Arduino, Fingerprint Sensor, RFID Reader, RFID Tag, GSM module, Relay, Motor, LED, and LCD.

Research Paper on Fingerprint Identification

Fingerprint identification is one of the most popular and reliable personal biometric identification methods. This paper describes an on-line fingerprint identification system consisting of image acquisition, edge detection, thinning, feature extractor, and classifier. The preprocessing part includes steps to acquire binarized and skeletonized ridges, which are needed for feature point extraction. Feature points (minutia) such as endpoints, bifurcations, and core points are then extracted, followed by false minutia elimination. Human fingerprints are rich in details called minutiae, which can be used as identification marks for fingerprint verification. The goal of this project is to develop a complete system for fingerprint identification.

Enhancing Electoral Integrity: A Fingerprint-Verified Voting System for Fair and Secure Elections

Voting is a significant method through which citizens in democratic nations like India can express their opinions. Voters often use polling booths to cast their ballots. Voting is now done via an electronic voting machine thanks to advancements in technology. This essay discusses a fingerprint-verified voting machine that is Internet of Things based. The primary goals of this project are to decrease voting malpractices and make voting safer with fingerprint verification. Voter information is kept in a database along with their fingerprint. The system verifies the user's Aadhaar number and, if verified, determines whether more than one vote has been cast if the fingerprint matches the stored fingerprint. A "Matching failed" message will appear if the fingerprint matching is incorrect, and an "Aadhar not match" message will appear if the Aadhaar number is incorrect. The controller used in this project is an Arduino Uno. The user's fingerprint is used for authentication. Everybody's fingerprints differ from one another, if not slightly. A notification stating "Already voted" will appear in the event of malpractice. The ballot card is displayed and the result is stored on the cloud using the Arduino IDE for board programming. Only authorized voters may cast ballots, and the system alerts users to instances of fraud. This project ensures fair elections and protects citizens' right to vote.

Balancing Accuracy and Error Rates in Fingerprint Verification Systems Under Presentation Attacks With Sequential Fusi

Balancing Accuracy and Error Rates in Fingerprint Verification Systems Under Presentation Attacks With Sequential Fusi

High Precision Fingerprint Verification for Small Area Sensor Based on Deep Learning

High Precision Fingerprint Verification for Small Area Sensor Based on Deep Learning

Cryptographic Security Approach for Biometric Verification System

This paper presents cryptography which is the science of encryption and decryption to protect fingerprint that can be transmitted from sender to receiver. This security approach (cryptography) can also be applicable to other biometric traits like face, iris, retina and palm print. The significant of this protective medium is to prevent intruders or attacker to easily perceive the presence of fingerprint images. The method that was utilized for implementation of this cryptographic security approach for fingerprint verification System was achieved through Elliptic Curve Cryptography and Hill Cipher Algorithm. An elliptic Curve Function was defined and its domain parameters were used to generate self invertible key matrix that was used for the encryption and decryption process of the biometric images (Fingerprint, Face and Ear ) The security approach was also improved by ensuring that the decryption can only be done through secret key. After decryption, fingerprint verification was done by extracting and matching distinct feature (ridges) from template fingerprint and distinct feature from input fingerprint. The result obtained from demonstration of the cryptographic approach allows end users to load fingerprint image, encrypt it at sending end. At receiver end, end user can decrypt the fingerprint image. Image enhancement was later done before authentication through extraction and matching of distinct features. This model will prevent destruction and manipulation of stored fingerprint image. Authentication can also be accomplished through biological traits instead of use of password that can be stolen or transferred to relative.

AlexNet Convolutional Neural Network Architecture with Cosine and Hamming Similarity/Distance Measures for Fingerprint Biometric Matching

In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compared to traditional image filtering techniques. This paper aimed to utilize a specific CNN architecture known as AlexNet for the fingerprint-matching task. Using such an architecture, this study has extracted the significant features of the fingerprint image, generated a key based on such a biometric feature of the image, and stored it in a reference database. Then, using Cosine similarity and Hamming Distance measures, the testing fingerprints have been matched with a reference. Using the FVC2002 database, the proposed method showed a False Acceptance Rate (FAR) of 2.09% and a False Rejection Rate (FRR) of 2.81%. Comparing these results against other studies that utilized traditional approaches such as the Fuzzy Vault has demonstrated the efficacy of CNN in terms of fingerprint matching. It is also emphasizing the usefulness of using Cosine similarity and Hamming Distance in terms of matching.

Dual-mode luminous and afterglow Ca3Al2Ge3O12:Yb3+, Er3+ phosphors for anti-counterfeiting and fingerprint verification

Generally, traditional anti-counterfeiting fluorescent materials usually show a single peak under single wavelength excitation, which cannot meet the security of authentication information. Therefore, the simultaneous realization of multicolor, multiexcitation and multimodal luminescence properties remains a fascinating goal, especially in a single host. In this work, the sustained luminescent materials Ca3Al2Ge3O12:Yb3+, Er3+ (CAG:Yb3+, Er3+) were obtained, which can exhibit bright red and green luminescence with Er3+ ions under the 490 nm and near-infrared (980 nm) light excitation. The red emission dominates under the 980 nm laser irradiation, and the sample showed excellent photoluminescence (PL) and persistent luminescence (PersL) performance under the 490 nm irradiation, and the afterglow can exceed 6h. The up-conversion mechanism and emission properties of the phosphors were analytically studied, and some fluorescence-safe anti-counterfeiting patterns and multifunctional latent fingerprint (LFP) afterglow time imaging were designed as potential applications for anti-counterfeiting aspects and information storage.

Development of an Improved Multi-filtering Matching Model for Fingerprint Recognition

Over the years research done in the area of fingerprint recognition in which the hybrid matching algorithm is one of the most common techniques, though the hybrid algorithm performed well but still faced with the challenge of false minutiae. This study formulated, simulated, and evaluated a multi-filtering fingerprint matching model to develop a multi-filtering matching model for fingerprint recognition. The method employed a multi-filtering model that was formulated using image pre-processing; minutiae feature extraction, post-processing, and cancellation of false minutiae algorithms in the processed images. The model was simulated using Matlab and fingerprint images from the Fingerprint Verification Competition (FVC) 2002 database. The performance of the model was evaluated using the False Acceptance Rate (FAR), False Rejection Rate (FRR), and Error Equal Rate (EER). The results showed that the false minutiae cancellation algorithm considerably reduced the false minutiae points in the thinned images which resulted in the reduction of false acceptance when two different images were tested, and also reduction in false rejection rate when two same images were tested. The match score was below the threshold value of 50 for false acceptance rate and above the threshold value of 50 for the false rejection rate. The error equal rate EER value of 0.076 was recorded. The study concluded that there was a significant reduction in the false minutiae points present in the thinned images and that a high accuracy of fingerprint matching was achieved when the datasets include poor quality fingerprint images.

Chaos and multi-layer attractors in asymmetric neural networks coupled with discrete fractional memristor

This article introduces a novel model of asymmetric neural networks combined with fractional difference memristors, which has both theoretical and practical implications in the rapidly evolving field of computational intelligence. The proposed model includes two types of fractional difference memristor elements: one with hyperbolic tangent memductance and the other with periodic memductance and memristor state described by sine functions. The authenticity of the constructed memristor is confirmed through fingerprint verification. The research extensively investigates the dynamics of a coupled neural network model, analyzing its stability at equilibrium states, studying bifurcation diagrams, and calculating the largest Lyapunov exponents. The results suggest that when incorporating sine memristors, the model demonstrates coexisting state variables depending on the initial conditions, revealing the emergence of multi-layer attractors. The article further demonstrates how the memristor state shifts through numerical simulations with varying memductance values. Notably, the study emphasizes the crucial role of memductance (synaptic weight) in determining the complex dynamical characteristics of neural network systems. To support the analytical results and demonstrate the chaotic response of state variables, the article includes appropriate numerical simulations. These simulations effectively validate the presented findings and provide concrete evidence of the system’s chaotic behavior.

Generation of Secure Fingerprint Template Using DFT for Consumer Electronics Devices

Biometric authentication systems are being extensively used to secure various applications. Consumer electronics devices also utilize biometrics as one of the authentication methods. However, this growth in the use of biometric based authentication may lead to the permanent loss of the biometric identity of a person if it is compromised, as biometric data cannot be changed. Hence, this poses the requirement of securing user templates in biometric based systems. In this paper, we propose a secure, alignment-free, and non-invertible fingerprint based user template protection technique which utilizes the mapping of pair-polar structures of minutiae in a 3D-grid and Discrete Fourier Transform (DFT). The proposed technique has been analyzed in terms of revocability, diversity, security, and performance by using four publicly available fingerprint databases of Fingerprint Verification Competition (FVC) 2002 and 2004. In addition to these databases, a partial fingerprint database generated from the FVC2002 DB1 is also utilized. The Equal Error Rate (EER) values of the proposed technique for all the databases are compared with that of the state-of-the-art techniques, and the superiority of the results depicts the robustness and effectiveness of the proposed technique.

Biometric-Based Electronic Voting System

Abstract: This project presents the design and implementation of an electronic voting machine (EVM) that uses fingerprint verification to enhance the security and accuracy of the voting process. The EVM is integrated with an SQLite database to store voters' fingerprint data and personal details. The system allows voters to cast their votes by verifying their identity through fingerprint recognition and pressing a button on the EVM. Once a vote is cast, the voter receives a message on their mobile device to confirm that their vote has been recorded. The project aims to address the challenges of traditional voting systems and offer a more secure and reliable method for election management.

Adaptation of Pair-Polar Structures to Compute a Secure and Alignment-Free Fingerprint Template

The unalterable nature of fingerprint biometrics leads to permanent identity loss of a user if an original fingerprint template is compromised. Moreover, it is evident in the literature that reconstructing a fingerprint image from an original fingerprint template is a feasible task. In order to protect the fingerprint template, we propose a noninvertible and alignment-free fingerprint template protection technique by exploiting the pair-polar minutiae structures. In the proposed technique, a secure fingerprint template is generated in the form of a set of binary vectors. These vectors are computed from the many-to-one mapping of the transformed pair-polar coordinates into a 3-D grid. Results of the proposed technique are analyzed in terms of revocability, unlinkability, security, and performance on six publicly available databases of Fingerprint Verification Competition 2002 and 2004. The overall analysis of the results clearly shows the effectiveness of the proposed technique as compared to the existing state-of-the-art techniques.

A hierarchical heterogeneous ant colony optimization based fingerprint recognition system

Personal identification is crucial to secure data against cyber-attacks. With increasing identity theft, fingerprint recognition systems have a growing importance in enforcing security and reliable identification. Although, most fingerprint recognitions systems use minutiae features for fingerprint matching, they require extensive pre-processing of fingerprints when the image quality is poor. This may introduce false ridge patterns, degrading the performance of the system. Moreover, fingerprint matching over a large database can be inefficient due to high computation time of fingerprint matching algorithms. This demand for fingerprint recognition systems that are fast and reliable. This paper proposes a computationally intelligent fingerprint recognition system that extracts ridge patterns from the fingerprint for matching. Hierarchical Heterogeneous Ant Colony Optimization based Fingerprint Matching (HHACOFM) algorithm has ant agents at different levels in the hierarchy to find a match between the input and stored ridge patterns. The algorithm was evaluated over four databases: a synthetic database generated using SFinGe tool, an internal database, SOCOFing database and FVC2004 database. Experimental results indicate that the proposal achieves high recognition rate compared to the existing approaches. HHACOFM algorithm achieves less EER than the state-of-art approaches. The results were validated using statistical tests. HHACOFM enables parallelism and thus reduces the response time. The proposal is scalable and suitable for real time applications demanding fast fingerprint verification.

CoRAE: Energy Compaction-Based Correlation Pattern Recognition Training Using AutoEncoder

Automatic Target Recognition (ATR) using Correlation Pattern Recognition (CPR) in IoT-based applications encounters limitations like limited memory and inadequate computational resources. One reason is the required quantity of reference templates for each target/object to cover all features of a target/object. To mitigate the issue of reference templates per target/object without accuracy degradation, this paper proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">energy compaction-based CPR autoencoder-training</i> . Additionally, a newly proposed performance metric known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Peak Energy Gain (PEG)</i> estimates the quality of the correlation plane and the feature compression capability CPR methods. The proposed, composite filtering strategy, Eigen Maximum Average Correlation Height (EMACH), and Extended Eigen Maximum Average Correlation Height ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> MACH) are vigorously validated using publicly available biometric and object image databases. By training a single reference template, the proposed training method achieves 97.97% mean accuracy with the second-best approach of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> MACH that attains 53.04% mean accuracy on the Pose Estimation Database. For bio-metric fingerprint verification, the mean Equal Error Rate (EER) of the proposed approach and the composite strategy is 3% and 29.69%, respectively on the FVC2002DB1A database. Similarly, the mean EER of the proposed approach and the composite strategy is 10.55% and 26.32%, respectively on the FVC2006IA database. For FEI faces dataset, the proposed method achieves 1.41% mean EER, and the composite filtering approach achieves 21.43% mean EER. On the University of Tehran Iris database, the proposed autoencoder-based methodology obtains 19.07%, and 18.07% mean EER on the left and right side iris instances, respectively. The comparative results for each dataset demonstrate superiority of AE-based method over the state-of-the-art CPR methods.

Biometric technology for personal identification and authentication: fingerprint

This paper presents an efficient fingerprint verification algorithm that improves matching accuracy. Algorithms for image processing for biometric technologies of identification and authentication of a person using fingerprints are considered. They are essential tools for improving performance across a wide range of industries. The recognition system is designed for storing data, further processing it, identifying and displaying fingerprints. In forensics, modern image processing algorithms are used and provide a significant improvement in work results. Fingerprint images are degraded and damaged due to changes in skin and print conditions. Thus, image enhancement techniques are used before feature points are detected and fine details are extracted. Templates are generated based on feature vector extraction and databases are created for verification and identification of fingerprint images. The method of experimental research is carried out, the process of processing the identification results is considered. To solve this problem, a recognition system was proposed, which is designed to store data, further process it, identify and display fingerprints. They are used in places of high secrecy as a kind of password access key based on scanning and verifying fingerprints with the FVC2002 database. The results obtained show that the proposed algorithm is more efficient and reliable.