Biometric Fingerprint Research Articles

A Novel Technique for Fingerprint Based Secure User Authentication

Fingerprint biometrics are extremely popular among digital identity systems for authentication. Most fingerprint based authentication systems store unique minutiae points information of a user directly in the database as their template. An adversary can potentially attack the user template that could compromise information of minutiae points. It has been proven that a fingerprint can be reconstructed from minutiae points and hence there is an important requirement in fingerprint based biometric systems to provide protection and privacy. This article introduces a novel technique to provide enhanced protection for fingerprint based biometrics, taking into account the attributes of minutiae point, the ridge count between the neighbouring minutiae points and the local minutiae point structure. The proposed technique has been implemented and tested on a total of nine fingerprint databases of varying quality. In comparison to the other existing techniques under different scenarios, the proposed technique exhibits superior recognition performance. The proposed technique is a singular point independent and has shown high stability, robustness, and security.

An Investigation of Benford’s Law Divergence and Machine Learning Techniques for Intra-Class Separability of Fingerprint Images

Protecting a biometric fingerprint database against attackers is very vital in order to protect against false acceptance rate or false rejection rate. A key property in distinguishing biometric fingerprint images is by exploiting the characteristics of these different types of fingerprint images. The aim of this paper is to perform an intra-class classification of fingerprint images using Benford's law divergence values and machine learning techniques. The usage of these Benford’s law divergence values as features fed into the machine learning techniques has proved to be very effective and efficient in the intra-class classification of biometric fingerprint images. The effectiveness of our proposed methodology was demonstrated on five datasets resulting in a total of 367 samples. All the machine learning techniques used in this experiment were trained using the k-fold cross validation and the dataset was split into ten times (10-folds). The models achieved high intra-class classification mean accuracies of 99.72% for the Convolutional Neural Networks (CNN), and 95.90% for the Naïve Bayes. Again, the Decision Tree and Logistic Regression, achieved accuracies of 95.62%, and 94.47%, respectively. These results showed that Benford’s law features and machine learning techniques, especially the CNN and Naïve Bayes can be effectively applied for the intra-class classification of fingerprint images. The implication of these results is that the different types of fingerprint images can be effectively discriminated using Benford's law divergence values and machine learning technique for forensics and biometrics applications.

Development of individual identification method using thoracic vertebral features as biometric fingerprints

Identification of individuals is performed when a corpse is found after a natural disaster, incident, or accident. DNA and dental records are frequently used as biometric fingerprints; however, identification may be difficult in some cases due to decomposition or damage to the corpse. The present study aimed to develop an individual identification method using thoracic vertebral features as a biological fingerprint. In this method, the shortest diameter in height, width, and depth of the thoracic vertebrae in the postmortem image and a control antemortem were recorded and a database was compiled using this information. The Euclidean distance or the modified Hausdorff distance was calculated as the distance between two points on the three-dimensional feature space of these measurement data. The thoracic vertebrae T1-12 were measured and the pair with the smallest distance was considered to be from the same person. The accuracy of this method for identifying individuals was evaluated by matching images of 82 cases from a total of 702 antemortem images and showed a hit ratio of 100%. Therefore, this method may be used to identify individuals with high accuracy.

PClusBA: A Novel Partition Clustering-Based Biometric Authentication Mechanism

In any communication paradigm, authentication plays an important role. Common authentication methods such as passwords are vulnerable to brute force attacks and cryptanalysis. Authentication mechanisms based on biometrics prove to be a better alternative owing to their permanence, uniqueness, and non-reproducibility. Fingerprint biometrics is more suitable in the context of network security applications due to its uniqueness and ease of processing. In general, each fingerprint has 30–40 unique points. The lesser the number of unique points extracted from the fingerprint, the more flexible it is to represent a Unique ID (UID). Clustering can be used to prune the unique point count. Since partition clustering gives us the choice of selecting the cluster count apriori, we have used partition clustering in the proposed biometric authentication mechanism named PClusBA. The cluster count is set to eight to obtain a UID of 192 bits in the proposed format. The length of the UID conforms to the NIST standards for enhancing security in commercial applications. The algorithm was implemented and tested using images from DB1 FVC 2002 database and the results were found to be unique for each image.

Are Biometric Attendance Registers a Panacea for Workplace Absenteeism in Tanzania? A Lesson from Public Secondary Schools in Nyamagana Municipality

Teachers’ workplace absenteeism is widely reported in countries like Tanzania. Left unaddressed, the problem negatively affects institutions and students’ academic excellence. In response, use of digital technology, including deployment of biometric fingerprint attendance registers is thought to bring a novel opportunity for mitigating the problem. However, the extent to which biometrics is effective in managing employees’ attendance in countries like Tanzania is yet to be known. This study draws experience from Nyamagana Municipality to ascertain the effectiveness of biometric fingerprint attendance registers in addressing teachers’ workplace absenteeism. The findings unveil challenges that blur the effectiveness of biometric fingerprint attendance registers in the studied district. With ICT, teachers can still falsify their workplace attendance. More so, unreliable power supply and COVID-19 pandemic have heightened the challenges related to effective utilisation of the fingerprint machines in managing teacher absenteeism. The study recommends that an effective design and use of digital technology in managing teachers absenteeism should take into account the sociological aspects in educational industry.

REVIEW ON COMBINING BIOMETRIC AND BEHAVIORAL AUTHENTICATION TECHNIQUES FOR SECURE MOBILE CLOUD COMPUTING

Cloud computing is one of the widely used technology and its use is increasing day by day due to its sharing of services over the internet. The area of concern is its security and privacy issues especially for the smart-phone users. Several security approaches had been introduced to safeguard users from unauthorized access but the security standard with the smart-phone devices are still not up to the mark and uses the typical username and passwords based authentication techniques. This paper is proposing a hybrid authentication approach combining two security modalities i.e. biometric and behavioral keystroke dynamics based user authentication which is designed to provide authentication with the existing username and password along with the biometric and behavioral keystroke dynamics based authentication with the use of biometric fingerprint scanner and keyboard thereby reducing the high cost of additional hardware devices since both are the integral part of the smart-phones these days. This approach will provide a secure access to the smart-phone users for a secure cloud environment efficiently than the existing methods.

Finger print vehicle starter

Fingerprint recognition technology allows access to only those whose fingerprints that are pre stored in the memory. Stored fingerprints are retained even in the event of complete power failure or battery drain. These eliminates the need for keeping track of keys or remembering a combination password, or PIN. It can only be opened when an authorized user is present, since there are no keys or combination to be copied or stolen, or locks that can be picked. The fingerprint based lock therefore provides a wonderful solution to conventionally encountered inconveniences. Biometric system includes various types such as face recognition, voice recognition, fingerprint recognition, eye recognition. Among these techniques the fingerprint recognition is the most widely used. This is because fingerprint of every person on the earth is unique and can provide good reliability compared to the other conventional methods. Fingerprint biometrics are easy to implement. The two significant parts of fingerprint biometric system is Identification and Authentication . Keywords: Fingerprint sensor, Arduino UNO, LCD, Relay, Buzzer.

How much is privacy worth around the world and across platforms?

AbstractUsing carefully designed discrete choice surveys, we measure individuals' valuation of online privacy across countries (United States, Mexico, Brazil, Colombia, Argentina, and Germany) and data types (personal information on finances, biometrics, location, networks, communications, and web browsing). We find that Germans value privacy more than do people in the United States and Latin American countries. Across countries, people most value privacy for financial (bank balance) and biometric (fingerprint) information. People had to be paid the least for permission to receive ads—respondents in Argentina, Colombia, and Mexico would even pay for them—followed by location privacy. We discuss privacy policy implications.

Few-shot driver identification via meta-learning

Driver identification in connected transportation is useful for usage-based insurance, personalized assisted driving, fleet management etc. Capturing the driving style from data behind the wheel benefits such applications without requiring extra costs and offending drivers’ biometric fingerprint privacy (e.g., facial recognition). However, the driver group to be identified may change, which leads to poor learning ability in a method based on depth representations of driving styles for new drivers and produces a sharp decline in generalization ability. Meta-learning is an exciting subfield of machine learning that equips deep learning models with the ability to learn, especially when the given data sample is very limited. This paper addresses a distinctively novel driver identification problem, where the prior model is supposed to quickly adapt to varying numbers of drivers, especially when few examples are available. First, based on a public driving dataset, a set of training and testing tasks is specifically designed for few-shot driver identification. Then, based on the popular model-agnostic meta-learning (MAML) framework, a feature autoencoder regularized learner is proposed to avoid the commonly encountered memorization problem and improve the generalization ability of the identification model. Three versions of meta-models are derived concerning computation and classification effectiveness. Finally, the experimental results show that the proposed method is superior to the previous baselines.

A blockchain and smart contract-based data provenance collection and storing in cloud environment

Data uploading needs security and privacy in the cloud. But there are some problems like centralized provenance data (PD) collection, storage, lack of security, integrity, and more time consumption. There are methods like Rabin, Knapsack, McEliece, Elagamal, and Rivest–Shamir–Adleman for the generation of keys but it increases the encryption and decryption time and less security. Therefore, the blockchain and smart contract-based data provenance (BSCDP) Architecture is proposed for providing secure storage in the cloud environment. Initially, the fingerprint biometrics and physically uncloneable functions (PUF) have been used in verification process. The combination of PUF and fingerprint biometrics is used for secure data transmission. To protect privacy and strengthen the security, the fuzzy extractor is employed. Secondly, we use an elliptic-curve key based cyclic shift transposition cryptography algorithm for enhancing the security when sharing the key. Thirdly, we introduce the blockchain and the interplanetary file system (IPFS) for PD collection, hash computation, and storing with reduced computational overhead (CO). The integrity of data is maintained by using blockchain based secure hashing algorithm-3. By arranging fuzzy based smart contracts (FSC), the data user (DU) tracks their data. FSC is employed for tracking the history of data. The data collected is directly stored in IPFS and the DU gets a hash from IPFS to retrieve the data in the future. Finally, the data verification is done by the provenance auditor. When comparing our proposed BSCDP method with existing methods, the proposed BSCDP method achieves high security in the cloud environment for 2 K users in terms of evidence insertion time (30 ms), verification time (30 ms), response time (40 ms), total change rate (5%), CO (5.8 Kb), encryption (50 ms) and decryption time (52 ms).

Novel Robust Photoplethysmogram-Based Authentication

As the tremendous advances in the biosensing technology, the biometric authentication approach has been adopted to replace the conventional password-based systems. Due to their harmless nature, photoplethysmogram (PPG) related applications have been quite intriguing in recent years. PPG can measure volumetric blood-flow changes in the peripheral circulation. Because PPG signals are very easy to acquire, compared to other biometrics (fingerprints, iris/retina, etc.), especially by low-cost wearable electronic devices, they are widely adopted to measure heart rates. In this paper, we devise a novel robust PPG based authentication system, which is capable of continuously authenticating the user instead of one-time authentication as carried out by the conventional techniques. This new system consists of preprocessing and filtering, motion artifact (MA) removal, template and feature extraction, and training. The associated multiwavelet-based feature extraction mechanism facilitates more reliable features than the conventional scalar-wavelet schemes so that it allows the learning model to better distinguish users. The associated autoencoder perfectly transforms the input PPG signal into the latent space. Finally, any distance measure can be adopted to classify or authenticate users. In order to build a sufficiently large dataset, we have combined three public datasets and our own dataset collected by ourselves locally together to create a new dataset consisting of 120 subjects totally. We have already built the hardware prototype successfully for real-time training data collection and real-time authentication. The equal-error-rate at the authentication stage reaches 5.5% and the identification accuracy reaches 98% at the identification stage.

Two-factor-based RSA key generation from fingerprint biometrics and password for secure communication

In an asymmetric-key cryptosystem, the secure storage of private keys is a challenging task. This paper proposes a novel approach for generating the same public and private key pair on a need basis. Hence, the need for secure storage of the private key is done away with. The proposed approach for generating the key pair is based on two factors: fingerprint biometrics and password. A stable binary string is generated from the distances among pairs of minutiae points in a fingerprint using a gray code-based method. Experiments show that gray code representation significantly reduces the number of inconsistencies between the generated bit strings from two instances of the same fingerprint as against the binary code representation. Hence, the Reed–Solomon error correction code successfully corrects errors due to variations in multiple instances of the same fingerprint to induce stability in the generated string. Hash of the stable string generated from the fingerprint and the string generated from hashed password are XORed to derive a stable seed value. The proposed approach uses this seed value to generate two large prime numbers. These prime numbers are used to generate the public and private key pair using the RSA key generation method. This seed value ensures the generation of the same key pair every time. The experimental results show that the proposed approach can ensure a stable generation of the key. It is not required to store either the fingerprint template or the password. Moreover, the generated private key is also not stored. It can be regenerated on a need basis.

Designing a biometric fingerprint scanner-based, secure and low-cost electronic voting machine for India

A reliable electronic voting machine (EVM) is proposed and implemented in this study, which is integrated with a biometric fingerprint scanner to ensure a secure election process. This biometric EVM includes features such as an interactive user interface, hack-free design and master lock. The EVM system has the capability of registering user data and storing it in a database through proper authentication. Moreover, the system proposed lowers the requirement for human resources. This paper provides a detailed description of the systematic development of the hardware and software used. The software part includes algorithm development and implementation. A thorough and in-depth understanding of the data and the communication protocols along with the pathways used for storage of data in the devices is provided. Additionally, the cost of the system is 62.82% less than the officially existing EVM machines in India.

The PLUS Multi-Sensor and Longitudinal Fingerprint Dataset: An Initial Quality and Performance Evaluation

In order to assess longitudinal effects in fingerprint biometrics several studies have been carried out in the last decade. Almost all of these investigations focused on non-public forensic fingerprint datasets because there is hardly any publicly available fingerprint database allowing experiments and a corresponding analysis of longitudinal aspects is often infeasible as the contained fingerprint samples are either captured in one session only or with a short time interval of only a few weeks between the sessions. With this work a new fingerprint dataset (108,106 samples) is introduced which can be used for inter-session (longitudinal) as well as inter-sensor fingerprint investigations. A total of ten different capturing devices based on distinct sensing technologies (optical, capacative, thermal, multispectral) is utilized and the imprints of all ten fingers of each of the 50 participating volunteers were acquired at four different time-separated sessions over 2 years. This enables inter-session as well as cross-device evaluations. Additionally, a first quality and recognition performance evaluation deploying several state-of-the-art minutiae based fingerprint recognition schemes and NFIQ 2.0 as quality measure is presented.

Artificial Intelligence Techniques Based Learner Authentication in Cybersecurity Higher Education Institutions

Education 4.0 is being authorized more and more by the design of artificial intelligence (AI) techniques. Higher education institutions (HEI) have started to utilize Internet technologies to improve the quality of the service and boost knowledge. Due to the unavailability of information technology (IT) infrastructures, HEI is vulnerable to cyberattacks. Biometric authentication can be used to authenticate a person based on biological features such as face, fingerprint, iris, and so on. This study designs a novel search and rescue optimization with deep learning based learning authentication technique for cybersecurity in higher education institutions, named SRODL-LAC technique. The proposed SRODL-LAC technique aims to authenticate the learner/student in HEI using fingerprint biometrics. Besides, the SRODL-LAC technique designs a median filtering (MF) based preprocessing approach to improving the quality of the image. In addition, the Densely Connected Networks (DenseNet-77) model is applied for the extraction of features. Moreover, search and rescue optimization (SRO) algorithm with deep neural network (DNN) model is utilized for the classification process. Lastly, template matching process is done for fingerprint identification. A wide range of simulation analyses is carried out and the results are inspected under several aspects. The experimental results reported the effective performance of the SRODL-LAC technique over the other methodologies.

Biometric Cryptosystem Based On Fingerprint Authentication And Cryptography Technique

Biometric systems are one of the most reliable and popular techniques today and fingerprint authentication is one of the most reliable and robust biometric techniques due to its nature. The characteristics of fingerprints play a big and important role in the authentication of people. In this research, fingerprint authentication scheme consists of many stages: image enhancement, binarization, segmentation, spine thinning, detail extraction. In this authentication we use Gaussian filter for better result. Hybrid protection is created through a combination of biometrics and cryptography, such as fingerprint and cryptography schemes. The combination of many biometric features with a single crypto key should offer an approach to increase authenticity and reduce the fake acceptance rate (FAR) and fake rejection rate (FRR) of fingerprints. For each new user of a biometric system, the combination of a cryptobiometric system will overcome the limitations of accuracy and vulnerabilities. We want to protect our real data from unauthorized people and systems, so we use cryptographic schemes as Elliptic Curve Diffie Hellman's key exchange algorithm. Biometric techniques can be used for various applications, such as:Biometrics can help make processes, transactions and everyday life safer and more convenient. You can use biometric data anywhere. to provide a valid identity solution. Cryptographic systems and fingerprint authentication have been identified as two of the most important aspects of the security environment. In this document, two powerful techniques are combined to produce better and safer results. In this study we use Gaussian Tyagi, S., Kumar, M., Singh, D. & Tyagi, H. (2023). IRJPMC; 1(1)– 27– filters, because less FAR and less FRR, with fingerprints authorized and finally authentication being a security key or a secure message created for a particular job . If the entered fingerprint matches the authorized person, but the DBA fingerprint does not, the system says "You are an unauthorized person, please try again." If the two fingerprints match, it will send all the secure passwords or cryptographic keys or secure messages for each work. It is developed by MATLAB (Matrix Laboratory). The proposed algorithm was tested on the FVC2004 database and compared with all participants in FVC2004. Keyword: Biometric systems, Fingerprint authentication, Image enhancement, Cryptography, Gaussian filter, FAR, and FRR.

Secure Cancelable Template Based on Double Random Phase Encoding and Entropy Segmentation

In this paper, a proposed cancellable biometric scheme is based on multiple biometric image identifiers, Arnold’s cat map and double random phase encoding (DRPE) to obtain cancellable biometric templates. The proposed segmentation scheme that is used to select the region of interest for generating cancelable templates is based on chaos entropy low correlation statistical metrics. The objective of segmentation is to reduce the computational cost and reliability of template creation. The left and right biometric (iris, fingerprint, palm print and face) are divided into non-overlapping blocks of the same dimensions. To define the region of interest (ROI), we select the block with the highest entropy. To shorten the registration process time and achieve a high level of security, we select 25% of the image volume of the biometric data. In addition, the low-cost security requirement lies in the use of selective encryption (SE) technology. The step of selecting the maximum entropy is executed on all biometric blocks. The maximum right and left multi-biometric blocks are arranged in descending order from the entropy perspective and select 50% of each biometric couple and store the single matrix. The obtained matrix is scrambled with a certain number of iterations using Arnold’s Cat Map (ACM).The obtained scrambled matrix is encrypted with the DRPE to generate the cancellable biometric templates, which are further concatenated. The simulation results display better performance of the suggested cancellable biometric system in noise scenarios using the area under the receiver operating characteristic (AROC). The strength of the suggested technique is examined with correlation, irregular deviation, maximum difference and maximum deviation. The recommended proposed approach shows that the ability to distinguish the authentic and imposter biometrics of user seven in different levels of the noise environment.

Designing of a biometric fingerprint scanner-based, secure and low-cost electronic voting machine for India

Designing of a biometric fingerprint scanner-based, secure and low-cost electronic voting machine for India

A Secure and Efficient Multi-Factor Authentication Algorithm for Mobile Money Applications

With the expansion of smartphone and financial technologies (FinTech), mobile money emerged to improve financial inclusion in many developing nations. The majority of the mobile money schemes used in these nations implement two-factor authentication (2FA) as the only means of verifying mobile money users. These 2FA schemes are vulnerable to numerous security attacks because they only use a personal identification number (PIN) and subscriber identity module (SIM). This study aims to develop a secure and efficient multi-factor authentication algorithm for mobile money applications. It uses a novel approach combining PIN, a one-time password (OTP), and a biometric fingerprint to enforce extra security during mobile money authentication. It also uses a biometric fingerprint and quick response (QR) code to confirm mobile money withdrawal. The security of the PIN and OTP is enforced by using secure hashing algorithm-256 (SHA-256), a biometric fingerprint by Fast IDentity Online (FIDO) that uses a standard public key cryptography technique (RSA), and Fernet encryption to secure a QR code and the records in the databases. The evolutionary prototyping model was adopted when developing the native mobile money application prototypes to prove that the algorithm is feasible and provides a higher degree of security. The developed applications were tested, and a detailed security analysis was conducted. The results show that the proposed algorithm is secure, efficient, and highly effective against the various threat models. It also offers secure and efficient authentication and ensures data confidentiality, integrity, non-repudiation, user anonymity, and privacy. The performance analysis indicates that it achieves better overall performance compared with the existing mobile money systems.

Case Study: Empirical Evaluation of a Biometric Credit Card for Fraud Reduction

To provide a more secure financial transaction, identity verification as a user authentication method can be realized through biometrics, most commonly, a fingerprint and can be achieved through the use of merchant touch screen credit card terminals or mobile purchasing applications. Using a physical credit card embedded with a fingerprint positions the user authentication process at the point of sale, thus providing real-time validation of the user as the credit card account owner utilizing the biometric fingerprint as identity proof and signature. This research seeks to evaluate the biometric credit card in an effort to increase the level of credit card transaction security and reduce the occurrences of fraud.