Biometric-based Authentication Research Articles

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.

An Integration of New Digital Image Scrambling Technique on PCA-Based Face Recognition System

Systems using biometric authentication offer greater security than traditional textual and graphical password-based systems for granting access to information systems. Although biometric-based authentication has its benefits, it can be vulnerable to spoofing attacks. Those vulnerabilities are inherent to any biometric-based subsystem, including face recognition systems. The problem of spoofing attacks on face recognition systems is addressed here by integrating a newly developed image encryption model onto the principal component pipeline. A new model of image encryption is based on a cellular automaton and Gray Code. By encrypting the entire ORL faces dataset, the image encryption model is integrated into the face recognition system’s authentication pipeline. In order for the system to grant authenticity, input face images must be encrypted with the correct key before being classified, since the entire feature database is encrypted with the same key. The face recognition model correctly identified test encrypted faces from an encrypted features database with 92.5% accuracy. A sample of randomly chosen samples from the ORL dataset was used to test the encryption performance. Results showed that encryption and the original ORL faces have different histograms and weak correlations. On the tested encrypted ORL face images, NPCR values exceeded 99%, MAE minimum scores were over (>40), and GDD values exceeded (0.92). Key space is determined byu2sizeA0where A0 represents the original scrambling lattice size, and u is determined by the variables on the encryption key. In addition, a NPCR test was performed between images encrypted with slightly different keys to test key sensitivity. The values of the NPCR were all above 96% in all cases.

Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model.

Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a deep learning-based algorithm is used for ear detection in 3D side face images. Second, a statistical ear model known as a 3D morphable ear model (3DMEM), was constructed to use as a feature extractor from the detected ear images. Finally, a novel recognition algorithm named you morph once (YMO) is proposed for human recognition that reduces the computational time by eliminating one-to-one registration between probe and gallery, which only calculates the distance between the parameters stored in the gallery and the probe. The experimental results show the significance of the proposed method for a real-time application.

Broken Heart: Privacy Leakage Analysis on ECG-Based Authentication Schemes

Authentications using biometrics, such as fingerprint recognition and electrocardiogram (ECG), have been actively used in various applications. Unlike traditional authentication methods, such as passwords or PINs, biometric-based authentication has an advantage in terms of security owing to its capability of liveness detection. Among the various types of biometrics, ECG-based authentication is widely utilized in many fields. Because of the inherent characteristics of ECG, however, the incautious design of ECG-based authentication may result in serious leakage of personal private information. In this paper, we extensively investigate ECG-based authentication schemes previously proposed in the literature and analyze possible privacy leakages by employing machine learning and deep learning techniques. We found that most schemes suffer from vulnerabilities that lead to the leakage of personal information, such as gender, age, and even diseases. We also identified some privacy-insensitive ECG fiducial points by utilizing feature selection algorithms. Based on these features, we present a privacy-preserving ECG-based authentication scheme.

Time dependency: an efficient biometric-based authentication for secure communication in wireless healthcare sensor networks

Time dependency: an efficient biometric-based authentication for secure communication in wireless healthcare sensor networks

Offline Handwritten Signature Recognition Using Deep Convolution Neural Network

In the modern age, technological advancement reached a new limit where authentication plays a vital role in security management. Biometric-based authentication is the most referenced procedure for authentication where signature verification is a significant part of it for authentication of a person. To prevent the falsification of signatures on important documents & legal transactions it is necessary to recognize a person's signature accurately. This paper focused on recognizing offline handwritten original & forged signatures using a deep convolution neural network. We use a completely new dataset & also downloaded datasets to train the system & verify a random signature as genuine or forgery. All testing samples are collected from several individuals after several steps of preprocessing the model is fed with the resultant image to our system, the experimental results give us an accuracy of 95.5% from the dataset.

A Research Paper on Biometric based ATM System

Abstract: Biometrics-based authentication is a potential alternative to password-based authentication. Of all biometric methods, face-based identification is one of the most convenient one. In ATM systems, facial images are captured using a high resolution camera. Bank security measures can play an important role in preventing customer attacks. These measures are of paramount importance in addressing the weaknesses of civil lawsuits. Banks must meet the criteria to provide their customers with a secure banking environment. This paper focuses on the increased vulnerability and criminal activity of automated teller machines (ATMs), not the bank themselves. Both customers and bankers. Keywords: Automated Teller Machine (ATM), Camera, Verification, Crime, E-Banking.

Biometric Authentication for Intelligent and Privacy-Preserving Healthcare Systems.

Secure identification is a critical system requirement for patients seeking health-related services. In the event of critical, aged, or disabled patients who require frequent health treatments, quick and easy identification is vital. Researchers describe the notion of the unprotected environment in this study, in which patients can receive health services from the hospital's smart and intelligent surroundings without the use of explicit equipment. Patients would interact directly with the environment and be identified through it. We suggest a biometric-based authentication technique for the unprotected hospital environment that also safeguards the patient's identity privacy. Furthermore, we demonstrate that this authentication technique is resistant to many well-known assaults, including insider attacks, replay attacks, and identity privacy. Doctors and other staff members showed enthusiastic responses after installing 2-factor authentications, as it makes their workflow efficient and makes things easier for patients. It also lets them focus on other factors rather than worrying about data security; hence, we need biometric authentication in intelligent and privacy-preserving healthcare systems. The paper deals with two-factor biometric authentication, and despite the added security, two-factor authentication adoption is said to be poor. It is due to a lack of awareness and difficulty to use and configure two-factor authentication (2FA) into a particular application by some individuals who struggle with the concept of authentication and its technology. Also, many 2FA methods in widespread use today have not been subjected to adequate usability testing. Research focuses on the point that there is still a large section of people unaware of the use of biometric systems to protect their online data. Researchers collected quantitative and qualitative data from 96 individuals during a two-week between-subjects usability survey of some common and rarely used 2FA approaches. The survey allowed the researcher to investigate which authentication methods are given higher priority and why, along with the relationship between different usage patterns and perceived usability, and identify user misconceptions and insecure habits to determine ease of use. It was observed that the biometric-based method was given the utmost preferability.

A Real-Time Biometric Encryption Scheme Based on Fuzzy Logic for IoT

When milliards of smart devices are connected to the Internet using the Internet of Things (IoT), robust security methods are required to deliver current information to the objects. Using IoT, the user can be accessed via smart device applications at any time and any place, which challenges IoT security and privacy. From security point of view, users and smart devices should have secure communication channel and digital ID. Authentication is the first step towards any security action. Biometric-based authentication can ensure higher security for developing secure access. In this paper, fingerprint is used as the biometric factor. After scanning the fingerprint using the cellphone’s camera, the image is transmitted to the authentication system. Since the comparison time increases after increasing the database volume, instead of storing the scanned fingerprint image, some key features of the scanned fingerprint are extracted and transmitted to the learning system of the convolutional neural network for detection and authentication and stored in the database. In the user authentication phase, the authentication keys are identified and the passcodes for the user of interest are extracted, and zero code is sent to the forged people; finally, the passcode is examined to check if the user is legal or illegal. In this step, the legal codes for fuzzy encoding of the image and text information are activated, and encryption is carried out in multiple steps depending on the number of members. The final code is compressed using Huffman coding and used for transmission to the network or storage. The proposed method is tested in MATLAB, and the results show that an excellent security is achieved using this cascade encryption method. Conclusively, the proposed hybrid coding technique reduces the information volume by 15.9%.

Biometric iris templates security based on secret image sharing and chaotic maps

Biometric technique includes of uniquely identifying person based on their physical or behavioural characteristics. It is mainly used for authentication. Storing the template in the database is not a safe approach, because it can be stolen or be tampered with. Due to its importance the template needs to be protected. To treat this safety issue, the suggested system employed a method for securely storing the iris template in the database which is a merging approach for secret image sharing and hiding to enhance security and protect the privacy by decomposing the template into two independent host (public) iris images. The original template can be reconstructed only when both host images are available. Either host image does not expose the identity of the original biometric image. The security and privacy in biometrics-based authentication system is augmented by storing the data in the form of shadows at separated places instead of whole data at one. The proposed biometric recognition system includes iris segmentation algorithms, feature extraction algorithms, a (2, 2) secret sharing and hiding. The experimental results are conducted on standard colour UBIRIS v1 data set. The results indicate that the biometric template protection methods are capable of offering a solution for vulnerability that threatens the biometric template.

Development, evaluation, and analysis of biometric-based bank vault user authentication system through brainwaves

Development, evaluation, and analysis of biometric-based bank vault user authentication system through brainwaves

Face-based Authentication Using Computational Secure Sketch

Biometric features are quite suitable for identity authentication due to its inherent properties – universality, uniqueness and persistence. In fact, biometric authentication has been widely used in our daily life, especially in mobile devices. However, biometric features are quite sensitive, and once a feature is leaked to an evil adversary, it cannot be used in authentication any more. This leads to a push on research of biometric privacy protection. In this paper, we propose a face-based authentication system with the help of a computational secure sketch. The computational secure sketch takes charge of error tolerance on the face samplings. Then the face features of the same user are used to extract an authentication key, which is in turn used to do the identity authentication for the user. The computational security of the computational secure sketch makes sure that the public information obtained by the adversary does not affect the pseudorandomness of the authentication key, hence the privacy of face features is guaranteed. Moreover, the privacy protection technique in our face-based authentication system can be extended to other biometric-based authentication.

Finger Vein Authentication Based on Wavelet Scattering Networks

Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two decades. Multiple biometrics such as fingerprint, palm, iris, palm vein and finger vein and other biometrics have been introduced. One of the challenges in biometrics is physical injury. Biometric of finger vein is of the biometrics least exposed to physical damage. Numerous methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification rate. This paper presents a novel method of scattering wavelet-based identity identification. Scattering wavelet extracts image features from Gabor wavelet filters in a structure similar to convolutional neural networks. What distinguishes this algorithm from other popular feature extraction methods such as deep learning methods, filter-based methods, statistical methods, etc., is that this algorithm has very high skill and accuracy in differentiating similar images but belongs to different classes, even when the image is subject to serious damage such as noise, angle changes or pixel location, this descriptor still generates feature vectors in a way that minimizes classifier error. This improves classification and authentication. The proposed method has been evaluated using two databases Finger Vein USM (FV-USM) and Homologous Multi-modal biometrics Traits (SDUMLA-HMT). In addition to having reasonable computational complexity, it has recorded excellent identification rates in noise, rotation, and transmission challenges. At best, it has a 98.2% identification rate for the SDUMLA-HMT database and a 96.1% identification rate for the FV-USM database.

Secure User Verification and Continuous Authentication Via Earphone IMU

Biometric plays an important role in user authentication. However, the most widely used biometrics, such as facial feature and fingerprint, are easy to capture or record, and thus vulnerable to spoofing attacks. On the contrary, intracorporal biometrics, such as electrocardiography and electroencephalography, are hard to collect, and hence more secure for authentication. Unfortunately, adopting them is not user-friendly due to their complicated collection methods or inconvenient constraints on users. In this paper, we propose a novel biometric-based authentication system, namely <i>MandiPass</i>. <i>MandiPass</i> leverages inertial measurement units, which have been widely deployed in portable devices, to collect intracorporal biometric from the vibration of user&#x0027;s mandible. It provides not only one-time verification function but also continuous authentication function. Both the two functions are secure and user-friendly. We theoretically validate the feasibility of <i>MandiPass</i> and develop a series of deep learning techniques for effective biometric extraction. We also utilize a Gaussian matrix to defend against replay attacks. Extensive experiment results with 34 volunteers show that <i>MandiPass</i> can achieve low equal error rate, even under various harsh environments.

Implementation of Efficient Biometric-Based Access for Secured Cloud Services

In our data-driven culture, the need for remote data storage and computing services has grown exponentially, requiring secure access to such data and services. This study proposes a new biometric-based authentication system for secure access to remote (cloud) servers. The proposed approach treats the user's biometrics as confidential credentials. It then uses the user’s biometric data to generate a unique ID and uses it to generate the user's private key. In addition, we present an efficient way to generate session keys for secure messaging between two interlocutors using two biometric templates. That is, you don't have to store your private and session keys somewhere. The proposed approach follows formal security analysis using detailed RealorRandom (ROR) model-based formal security analysis, informal (unmathematical) security analysis, and widely accepted automated Internet security verification., Can withstand multiple known attacks against (passive / active) attackers. Protocol and application (AVISPA) tools. Finally, numerous and comparative studies have shown the effectiveness and usefulness of the proposed approach.

ChestLive

Voice-based authentication is prevalent on smart devices to verify the legitimacy of users, but is vulnerable to replay attacks. In this paper, we propose to leverage the distinctive chest motions during speaking to establish a secure multi-factor authentication system, named ChestLive. Compared with other biometric-based authentication systems, ChestLive does not require users to remember any complicated information (e.g., hand gestures, doodles) and the working distance is much longer (30cm). We use acoustic sensing to monitor chest motions with a built-in speaker and microphone on smartphones. To obtain fine-grained chest motion signals during speaking for reliable user authentication, we derive Channel Energy (CE) of acoustic signals to capture the chest movement, and then remove the static and non-static interference from the aggregated CE signals. Representative features are extracted from the correlation between voice signal and corresponding chest motion signal. Unlike learning-based image or speech recognition models with millions of available training samples, our system needs to deal with a limited number of samples from legitimate users during enrollment. To address this problem, we resort to meta-learning, which initializes a general model with good generalization property that can be quickly fine-tuned to identify a new user. We implement ChestLive as an application and evaluate its performance in the wild with 61 volunteers using their smartphones. Experiment results show that ChestLive achieves an authentication accuracy of 98.31% and less than 2% of false accept rate against replay attacks and impersonation attacks. We also validate that ChestLive is robust to various factors, including training set size, distance, angle, posture, phone models, and environment noises.

Providing impersonation resistance for biometric-based authentication scheme in mobile cloud computing service

Mobile cloud computing (MCC) is an emerging computing model with the growing demands of mobile devices by services diversity using mobile cloud technology. Authentication plays a fundamental role between the smart portable devices located in different places and the mobile cloud computing service providers in MCC. Several authors presented many authentication schemes using different cryptographic approaches for MCC in the past years. However, those schemes have not solved the impersonation attack well. To conquer this suffering and support more security functionalities, we propose an impersonation resistance for a biometric-based authentication scheme using hashing and symmetric parameter function in an MCC environment. The proposed scheme can provide mutual authentication and anonymity using the software AVISPA. The security of the proposed scheme is formally proved in the random oracle model. Regarding functionality and performance analysis, the comparative studies demonstrate that the design has a trade-off between resource consumption and security strengthens for MCC services. Therefore, the proposed scheme is more practical in MCC application.