Biometric-based Authentication Research Articles

Biometric ATM Authentication System

As technology progresses, traditional authentication methods like Personal Identification Numbers (PINs) and passwords are becoming more prone to security breaches. To address this issue, biometric-based authentication systems are being seen as a reliable solution to boost security in different areas, such as Automated Teller Machines (ATMs). This study delves into the deployment, advantages, obstacles, and future potential of a biometric-based ATM system. By examining current research, real-life examples, and technological innovations, the aim of this paper is to shed light on the efficiency and viability of biometric authentication in ATM systems. Key Words: Enhanced Security, Authentication, Efficiency, Reliability

Reliable human authentication using AI-based multibiometric image sensor fusion: Assessment of performance in information security

Reliable human authentication is essential for assuring the availability, confidentiality, and integrity of sensitive data and resources in the field of information security. Due to the vulnerabilities of conventional authentication techniques like passwords and PINs, interest in biometric-based authentication systems has developed. To get over this, we suggest an efficient voice and iris recognition-based multimodal biometric verification strategy for human authenticating tools. We first collect the voice and iris datasets from 150 people. Then, we used a median filter and high pass filter to preprocess the voice and iris data. The features of voice identification are extracted using the shifted delta cepstral coefficient (SDCC) and the Mel frequency discrete wavelet coefficient (MFCC), and these two coefficients are evaluated. The outcomes of the extraction of iris identification features using Local binary pattern (LBP) and Speeded robust features (SURF) are evaluated. The classifier Fine Tuned Cuckoo Search Optimized Convolutional Neural Network (FCSO-CNN) performs voice and iris recognition modalities. Voice and iris biometric systems may be combined into a single multimodal biometric system by fusing their respective feature sets and scoring algorithms. The results of the computer simulation demonstrate that for speech recognition, using the SDC and MFC coefficients produces better results, while for iris recognition, using the LBP and FCSO-CNN experiment produces superior outcomes. Additionally, the scores fusion works superior to other scenarios in the proposed multimodal biometrics system. The suggested system provides a dependable and strong solution for human identification, assisting in the improvement of security measures across a variety of industries, including financial institutions, governmental agencies, and the defense of key infrastructure.

Biometric-Driven Digital Village Governance: A SWOC-Enabled Model with Fraud Prevention

In the digital age, the integration of advanced technologies and biometrics plays a pivotal role in transforming governance models. This research introduces an innovative approach to digital village governance, leveraging the potential of biometric data in biomedical applications while incorporating robust fraud detection mechanisms. The proposed model harnesses the power of Swarm Fish Optimization Classification (SWOC) to enhance the accuracy and efficiency of biometric-based authentication systems, ensuring the security of village governance processes. The study outlines the multifaceted components of the Intelligent Digital Village Governance Model, emphasizing its adaptability to the unique needs of rural communities. Central to this model is the utilization of biometric data, such as fingerprints, for user identification, access control, and the delivery of essential services. SWOC, with an optimization and classification algorithm inspired by swarm behavior, is integrated to refine the accuracy of biometric identification and detect fraudulent activities. Experimental results demonstrate the efficacy of SWOC in enhancing the accuracy of biometric-based authentication, thereby strengthening the security of digital village governance. The model's adaptability, scalability, and compliance with ethical standards are also discussed, ensuring responsible deployment in rural settings.

AI-powered biometrics for Internet of Things security: A review and future vision

Biometrics is a set of advanced technologies that use the physical or behavioral characteristics of individuals to provide reliable access control. With the rapid development in information and communication technology, biometrics has become an increasingly important field, especially with the integration of artificial intelligence (AI). Fingerprint- and facial-recognition technologies have been significantly improved by the use of AI. The widespread use of the Internet of Things (IoT) has led to security challenges and created new opportunities for biometrics to be used in various applications. This review provides a comprehensive analysis of intelligent or AI-powered biometrics, focusing on the integration of AI and biometrics for building security measures to improve IoT security. Due to their enhanced security and usability, biometric-based authentication methods are becoming increasingly popular in IoT environments. AI algorithms are essential for improving biometric systems in IoT applications by enabling advanced pattern recognition and adaptive decision-making. Furthermore, the integration of biometrics with AI and the IoT can help mitigate security risks, ensuring the protection of data and user privacy. This review makes three main contributions: it provides a comprehensive analysis of the interdependencies between the AI, biometrics, and IoT domains; it covers the applications of AI and biometrics in the context of the IoT; and it highlights the current challenges and future research directions for the deployment of intelligent biometrics in various IoT application domains. Furthermore, this review facilitates further research in the area of the application of intelligent biometrics in IoT applications.

Exploiting multimodal biometrics for enhancing password security

Abstract Digitization of every daily procedure requires trustworthy verification schemes. People tend to overlook the security of the passwords they use, i.e. they use the same password on different occasions, they neglect to change them periodically or they often forget them. This raises a major security issue, especially for elderly people who are not familiar with modern technology and its risks and challenges. To overcome these drawbacks, biometric factors were utilized, and nowadays, they have been widely adopted due to their convenience of use and ease of hardware installation. Many biometric-based authentication schemes were proposed, but despite the advantages that they offer, recent research has shown that biometrics by itself cannot be considered as an inviolable technique. Recently, we have proposed StegoPass, a novel method that obtains the 68 facial points of a user and utilizes them as a stego message to an image. This produced stego image was the password. Although the experiments conducted showed maximum security, it would be challenging to enhance the robustness of the proposed model for even more attacks. This paper examines the utilization of multimodal biometrics as the secret message embedded in the image. More specifically, besides the extraction of the facial points, we extract the unique minutiae moments and combine them in a feature vector. This feature vector is then embedded in the image. Two different datasets were used, and the security of the method was tested against state-of-the-art deep learning models, i.e. generative adversarial networks, to test whether the image could be digitally synthesized and fool the proposed verification scheme. The results proved that the new enhanced version of StegoPass offers an extremely secure method as its predecessor.

Toward Zero-Trust Security for the Metaverse

By focusing on immersive interaction among users, the burgeoning Metaverse can be viewed as a natural extension of existing social media. Similar to traditional online social networks, there are numerous security and privacy issues in the Metaverse (e.g., attacks on user authentication and impersonation). In this paper, we develop a holistic research agenda for zero-trust user authentication in social virtual reality (VR), an early prototype of the Metaverse. Our proposed research includes four concrete steps: investigating biometrics-based authentication that is suitable for continuously authenticating VR users, leveraging federated learning (FL) for protecting user privacy in biometric data, improving the accuracy of continuous VR authentication with multimodal data, and boosting the usability of zero-trust security with adaptive VR authentication. Our preliminary study demonstrates that conventional FL algorithms are not well suited for biometrics-based authentication of VR users, leading to an accuracy of less than 10%. We discuss the root cause of this problem, the associated open challenges, and several future directions for realizing our research vision.

Accurate authentication based on ECG using deep learning

Biometric-based authentication methods have been widely used, for example on portable devices (e.g., Android and iOS devices). However, there are several known limitations in existing authentication methods based on biometrics (e.g., those using facial, iris, and fingerprint). For example, in a healthcare context, a user may be physically incapable of completing the authentication due to his/her medical conditions. Hence, as a complementary authentication mechanism, there have been attempts to also utilize electrocardiogram (ECG). In this work, we propose an ECG authentication system that leverages deep learning. Specifically, to achieve generalization ability, complementary ensemble empirical decomposition (CEEMD) is introduced in our design. Moreover, a 1-D Multi-scale Convolutional Neural Network (1-D MCNN) is implemented to achieve accurate authentication. To evaluate the usability of our proposed approach, we have performed extensive experiments on eight databases, and the findings show that our proposed approach achieves good performance even on abnormal databases and can be adapted for different application environments. In addition, our adopted data from eight public databases requires theoretical statistical treatment for practical applications in real authentication scenarios.

A Secure, Flexible, and PPG-Based Biometric Scheme for Healthy IoT Using Homomorphic Random Forest

Advances in the Internet of Things such as biosensing and camera-capturing technologies have also resulted in biometric-based authentication approaches becoming more viable. Photoplethysmography (PPG), for example, can be leveraged to provide better biometric features for continuous authentication, in comparison to other biometrics such as fingerprint. However, the fewer morphological features in PPG signals can complicate the accurate authentication of PPG signals. Furthermore, one has to also consider transmission security and PPG template storage security. These two considerations are typically not taken into consideration in most existing PPG-based authentication schemes. Therefore, we design a secure PPG-based biometric system to achieve accurate authentication with biometric privacy protection. In our design, Homomorphic Random Forest is adopted to classify the homomorphically encrypted biometric features, thus protecting the user’s PPG biometrics from being compromised in authentication. Furthermore, beat qualification screening is set up to avoid the interference of unqualified signals, and 19 features with the least redundancy from the 541 features extracted are selected as the biometric features of the user. Doing so allows us to ensure the accuracy of authentication, as demonstrated in our evaluations using five PPG databases collected by three different collection methods (contact, remote, and monitor). In addition, our experiments adopt PPG signals acquired from remote cameras, which are not considered in other PPG-based biometric systems. The experimental results show that the average accuracy of our biometric system is 96.4%, the F1 score is 96.1%, the EER is 2.14%, and the authentication time is about 0.5s.

Face Recognition System Based Car Security System

Modern car security systems have been developed as a result of the growing demand in the automotive sector for increased convenience and security. In order to provide safe access management and authentication for cars, this paper suggests a face recognition system-based auto security system that makes use of facial recognition techniques. When a match is detected, the system uses computer vision algorithms to identify and detect the faces of approved drivers, enabling the car to be unlocked and started without any issues. In order to convert near infrared face images into their corresponding visible images, we develop a nir-vis image translation model. Using two distinct nir databases of face images, the pre-trained face embedding model is applied to present the evaluation performances. The proposed system offers improved security by eliminating the need for traditional key-based systems and providing a robust biometric-based authentication mechanism. Anti-spoofing procedures are also integrated to guarantee the system's dependability and defense against fraudulent attempts. One of the disadvantages of the current systems is that not everyone finds it comfortable to have to memorize their password for certain systems. An RFID card is another device that has been developed to verify the identification of the user. Hackers have the ability to modify RFID data and substitute it with their own. Certain systems utilize fingerprints to verify an individual's identity. The primary reasons for the low adoption of biometric fingerprint locks are their expensive cost and the potential for falsified or damaged fingerprints. Certain systems identify an obstruction ahead of the car, sound an alert, tell the driver to pull over, but do nothing to actually avoid the obstruction. Through tests and assessments, the usefulness and practicability of the suggested system are shown, highlighting its potential as a workable option for safe and practical access management in automotive settings

Privacy-preserving model for biometric-based authentication and Key Derivation Function

Bio-cryptosystems often save the biometric template for authentication and generally employ randomly generated keys to encrypt and sign data. This method raises privacy protection concerns. Furthermore, for a system secured by the usage of a cryptographic key, losing the key often has disastrous consequences. To overcome the privacy issues, and allow the secure recovery of lost keys, we design a Key Derivation Function to extract a key from biometric data: a new method – based on clustering algorithms – detects consistent and discriminative features from biometric characteristics to create a code. Then, HMAC-SHA256 (as specified by the National Institute of Standards and Technology) generates a standard key from the code. To reproduce the code at future times (that also serves for authentication), the Key Derivation Function stores helper data with the guarantee of privacy. Indeed, with a private face dataset, the probability of generating the code with only the helper data is less than 2−300, and less than 2−246 for a subset of the YouTube Face database. Moreover, on the private database and the tested users from the YouTube Face database, our system has a false acceptance rate of 0%. It corrects up to 40.3% of noise levels on the private database and has good management of the inter-user variability.

VibPath

Technical advances in the smart device market have fixated smartphones at the heart of our lives, warranting an ever more secure means of authentication. Although most smartphones have adopted biometrics-based authentication, after a couple of failed attempts, most users are given the option to quickly bypass the system with passcodes. To add a layer of security, two-factor authentication (2FA) has been implemented but has proven to be vulnerable to various attacks. In this paper, we introduce VibPath, a simultaneous 2FA scheme that can understand the user's hand neuromuscular system through touch behavior. VibPath captures the individual's vibration path responses between the hand and the wrist with the attention-based encoder-decoder network, authenticating the genuine users from the imposters unobtrusively. In a user study with 30 participants, VibPath achieved an average performance of 0.98 accuracy, 0.99 precision, 0.98 recall, 0.98 f1-score for user verification, and 94.3% accuracy for user identification across five passcodes. Furthermore, we also conducted several extensive studies, including in-the-wile, permanence, vulnerability, usability, and system overhead studies, to assess the practicability and viability of the VibPath from multiple aspects.

Deep Learning and Machine Learning, Better Together Than Apart: A Review on Biometrics Mobile Authentication

Throughout the past several decades, mobile devices have evolved in capability and popularity at growing rates while improvement in security has fallen behind. As smartphones now hold mass quantities of sensitive information from millions of people around the world, addressing this gap in security is crucial. Recently, researchers have experimented with behavioral and physiological biometrics-based authentication to improve mobile device security. Continuing the previous work in this field, this study identifies popular dynamics in behavioral and physiological smartphone authentication and aims to provide a comprehensive review of their performance with various deep learning and machine learning algorithms. We found that utilizing hybrid schemes with deep learning features and deep learning/machine learning classification can improve authentication performance. Throughout this paper, the benefits, limitations, and recommendations for future work will be discussed.

Graphical Password Authentication: Image Grid Based Digital Lock for Mobile Apps

Abstract: Over the past few decades, research on authentication methods has advanced significantly. While the initial focus was on using standardised password selection and management approaches to secure textual passwords, this was made possible by learning about the flaws in the existing systems based on the attacks performed on the same systems' vulnerabilities. With the rise of biometric-based authentication, the next stage in the study and development of improved solutions has begun. Although initially rather entertaining, this authentication method is very insecure. Once a person's biometrics have been stolen or replicated, new biometrics cannot be made. Compared to a person's one-step biometric authentication, biometrics in the multifactor authentication of an enterprise are less vulnerable. The development of graphical passwords is the result of all these issues with authentication systems. While multifactor graphical passwords impede smartphone users' usability and speedier processing-computational flexibility, overly simplistic graphical passwords are prone to shoulder surfing. Users of Android devices need an authentication system that is easy to use, quick, secure, and that gains faster access to the device. A study was done evaluating the usability, predictability, resistance to various attacks, and other characteristics of various graphical passwords. Grid-based graphical passwords outperformed their rivals among the different types of graphical passwords that were taken into consideration. The majority of graphical password implementations in the past have been on personal computers (PCs), while smartphone usage is far more prevalent than on PCs. We propose to develop an Android application with gridbased image-based graphical password authentication that will be secure and help a lot of users protect the secondary identities that they carry about in the form of smartphones. After evaluating which factors must remain in the system, the application was divided into five modules. Each module was first built using flowcharts, and later, the Java programming language was used to build the app in Android Studio.

An Experimental Analysis on Multicepstral Projection Representation Strategies for Dysphonia Detection.

Biometrics-based authentication has become the most well-established form of user recognition in systems that demand a certain level of security. For example, the most commonplace social activities stand out, such as access to the work environment or to one's own bank account. Among all biometrics, voice receives special attention due to factors such as ease of collection, the low cost of reading devices, and the high quantity of literature and software packages available for use. However, these biometrics may have the ability to represent the individual impaired by the phenomenon known as dysphonia, which consists of a change in the sound signal due to some disease that acts on the vocal apparatus. As a consequence, for example, a user with the flu may not be properly authenticated by the recognition system. Therefore, it is important that automatic voice dysphonia detection techniques be developed. In this work, we propose a new framework based on the representation of the voice signal by the multiple projection of cepstral coefficients to promote the detection of dysphonic alterations in the voice through machine learning techniques. Most of the best-known cepstral coefficient extraction techniques in the literature are mapped and analyzed separately and together with measures related to the fundamental frequency of the voice signal, and its representation capacity is evaluated on three classifiers. Finally, the experiments on a subset of the Saarbruecken Voice Database prove the effectiveness of the proposed material in detecting the presence of dysphonia in the voice.

Ear Recognition Using Rank Level Fusion of Classifiers Outputs

An individual's authentication plays a vital role in our daily life. In the last decade, biometric-based authentication has become more prevalent than traditional approaches like passwords and pins. Ear recognition has gained attention in the biometric community in recent years. Researchers defined several features for the identification of a person from ear image. The features play a vital role in the success of classification models. This paper considers an ensemble of features for designing a new classification model. The features are assessed in isolation as well as through feature-level fusion. Subsequently, a rank-level fusion for classification is introduced. The experiments are conducted on both constrained and unconstrained ear datasets. The results are promising and open up new possibilities in machine learning-based ear recognition

A hybrid learning scheme towards authenticating hand-geometry using multi-modal features

<span lang="EN-US">Usage of hand geometry towards biometric-based authentication mechanism has been commercially practiced since last decade. However, there is a rising security problem being surfaced owing to the fluctuating features of hand-geometry during authentication mechanism. Review of existing research techniques exhibits the usage of singular features of hand-geometric along with sophisticated learning schemes where accuracy is accomplished at the higher cost of computational effort. Hence, the proposed study introduces a simplified analytical method which considers multi-modal features extracted from hand geometry which could further improve upon robust recognition system. For this purpose, the system considers implementing hybrid learning scheme using convolution neural network and Siamese algorithm where the former is used for feature extraction and latter is used for recognition of person on the basis of authenticated hand geometry. The main results show that proposed scheme offers 12.2% of improvement in accuracy compared to existing models exhibiting that with simpler amendment by inclusion of multi-modalities, accuracy can be significantly improve without computational burden.</span>

Photoplethysmography Data Reduction Using Truncated Singular Value Decomposition and Internet of Things Computing

Biometric-based identity authentication is integral to modern-day technologies. From smart phones, personal computers, and tablets to security checkpoints, they all utilize a form of identity check based on methods such as face recognition and fingerprint-verification. Photoplethysmography (PPG) is another form of biometric-based authentication that has recently been gaining momentum, because it is effective and easy to implement. This paper considers a cloud-based system model for PPG-authentication, where the PPG signals of various individuals are collected with distributed sensors and communicated to the cloud for authentication. Such a model incursarge signal traffic, especially in crowded places such as airport security checkpoints. This motivates the need for a compression–decompression scheme (or a Codec for short). The Codec is required to reduce the data traffic by compressing each PPG signal before it is communicated, i.e., encoding the signal right after it comes off the sensor and before it is sent to the cloud to be reconstructed (i.e., decoded). Therefore, the Codec has two system requirements to meet: (i) produce high-fidelity signal reconstruction; and (ii) have a computationallyightweight encoder. Both requirements are met by the Codec proposed in this paper, which is designed using truncated singular value decomposition (T-SVD). The proposed Codec is developed and tested using a publicly available dataset of PPG signals collected from multiple individuals, namely the CapnoBase dataset. It is shown to achieve a 95% compression ratio and a 99% coefficient of determination. This means that the Codec is capable of delivering on the first requirement, high-fidelity reconstruction, while producing highly compressed signals. Those compressed signals do not require heavy computations to be produced as well. An implementation on a single-board computer is attempted for the encoder, showing that the encoder can average 300 milliseconds per signal on a Raspberry Pi 3. This is enough time to encode a PPG signal prior to transmission to the cloud.

A Secure Authentication Framework to Guarantee the Traceability of Avatars in Metaverse

Metaverse is a vast virtual environment parallel to the physical world in which users enjoy a variety of services acting as an avatar. To build a secure living habitat, it’s vital to ensure the virtual-physical traceability that tracking a malicious player in the physical world via his avatars in virtual space. In this paper, we propose a two-factor authentication framework based on biometric-based authentication and chameleon signature. First, aiming at disguise in virtual space, we design an avatar’s two-factor identity model to ensure the verifiability of avatar’s virtual identity and physical identity. Second, facing at authentication efficiency and keys holding cost, we propose a chameleon collision signature algorithm to efficiently ensure that the avatar’s virtual identity is associated with its physical identity. Finally, aiming at impersonation in the physical world, we design two decentralized authentication protocols based on the avatar’s identity model and the chameleon collision signature to achieve real-time authentication on the avatar’s identity. Security analysis indicates that the proposed authentication framework guarantees the consistency and traceability of the avatar’s identity. Simulation experiments show that the framework not only completes the decentralized authentication between avatars but also achieves virtual-physical tracking.

Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

Internet of Things (IoT) devices incorporate a large amount of data in several fields, including those of medicine, business, and engineering. User authentication is paramount in the IoT era to assure connected devices’ security. However, traditional authentication methods and conventional biometrics-based authentication approaches such as face recognition, fingerprints, and password are vulnerable to various attacks, including smudge attacks, heat attacks, and shoulder surfing attacks. Behavioral biometrics is introduced by the powerful sensing capabilities of IoT devices such as smart wearables and smartphones, enabling continuous authentication. Artificial Intelligence (AI)-based approaches introduce a bright future in refining large amounts of homogeneous biometric data to provide innovative user authentication solutions. This paper presents a new continuous passive authentication approach capable of learning the signatures of IoT users utilizing smartphone sensors such as a gyroscope, magnetometer, and accelerometer to recognize users by their physical activities. This approach integrates the convolutional neural network (CNN) and recurrent neural network (RNN) models to learn signatures of human activities from different users. A series of experiments are conducted using the MotionSense dataset to validate the effectiveness of the proposed method. Our technique offers a competitive verification accuracy equal to 98.4%. We compared the proposed method with several conventional machine learning and CNN models and found that our proposed model achieves higher identification accuracy than the recently developed verification systems. The high accuracy achieved by the proposed method proves its effectiveness in recognizing IoT users passively through their physical activity patterns.

Multimodal Behavioral Biometric Authentication in Smartphones for Covid-19 Pandemic

The usage of mobile phones has increased multi-fold in recent decades, mostly because of their utility in most aspects of daily life, such as communications, entertainment, and financial transactions. In use cases where users’ information is at risk from imposter attacks, biometrics-based authentication systems such as fingerprint or facial recognition are considered the most trustworthy in comparison to PIN, password, or pattern-based authentication systems in smartphones. Biometrics need to be presented at the time of power-on, they cannot be guessed or attacked through brute force and eliminate the possibility of shoulder surfing. However, fingerprints or facial recognition-based systems in smartphones may not be applicable in a pandemic situation like Covid-19, where hand gloves or face masks are mandatory to protect against unwanted exposure of the body parts. This paper investigates the situations in which fingerprints cannot be utilized due to hand gloves and hence presents an alternative biometric system using the multimodal Touchscreen swipe and Keystroke dynamics pattern. We propose a HandGlove mode of authentication where the system will automatically be triggered to authenticate a user based on Touchscreen swipe and Keystroke dynamics patterns. Our experimental results suggest that the proposed multimodal biometric system can operate with high accuracy. We experiment with different classifiers like Isolation Forest Classifier, SVM, k-NN Classifier, and fuzzy logic classifier with SVM to obtain the best authentication accuracy of 99.55% with 197 users on the Samsung Galaxy S20. We further study the problem of untrained external factors which can impact the user experience of authentication system and propose a model based on fuzzy logic to extend the functionality of the system to improve under novel external effects. In this experiment, we considered the untrained external factor of ‘sanitized hands’ with which the user tries to authenticate and achieved 93.5% accuracy in this scenario. The proposed multimodal system could be one of the most sought approaches for biometrics-based authentication in smartphones in a COVID-19 pandemic situation.