Biometric Identity Research Articles

Societal and Structural Risks of Biometric ID: Towards People’s Right to Privacy

Societal and Structural Risks of Biometric ID: Towards People’s Right to Privacy

Who Wants to be Legible? Digitalization and Intergroup Inequality in Kenya

Governments across the Global South have begun introducing biometric IDs (eIDs) in an attempt to improve citizen-state legibility. While such initiatives can improve government efficiency, they also raise important questions about citizen privacy, especially for groups with a history of mistrust in the state. If concerns about increased legibility produce differential uptake or changes in political behavior, eID initiatives may exacerbate societal inequalities. In a conjoint experiment with 2,072 respondents from four Kenyan regions, we examine how perceptions of and willingness to register for eID under different policy conditions vary across politically dominant, opposition, and “securitized” (heavily policed) ethnic groups. We find meaningful group-level variation in support for specific policy features, and suggestive evidence that policies facilitating surveillance may discourage opposition political participation. Our most surprising finding, however, is that there is such broad support for expanded legibility. The promise of access to government services appears to outweigh other considerations.

Deep Feed-Forward Neural Network-Based Biometric Authentication System with Biometric Identity and Reputation Score in Blockchain

The biometric authentication in today’s digital era has become a vital issue in security and privacy. Biometrics is currently becoming more significant specifically for higher security modules. Here, a deep feed-forward neural network-based biometric authentication system (DFFNN_biometric authentication system) is presented for biometric authentication utilizing a biometric fingerprint image. In blockchain network, biometric data are considered and the fingerprint images are fed as input. As an input fingerprint image, minutiae extraction is accomplished. Thereafter, a deep key is generated employing a deep residual network (DRN) for template protection. Thereafter, privacy protection template generation is conducted and the templates thus obtained are stored on a database (DB). On the other hand, a query fingerprint image is given as input in the authentication stage. The processes such as minutiae extraction, deep key generation using DRN and privacy protection template authentication are carried out as like it performed in the blockchain network. Additionally, the matching process is conducted between the output acquired in the authentication phase and image stored in DB. The reputation score is generated from authenticated biometric behavior. The DFFNN is utilized to generate a reputation score. Moreover, the DFFNN_biometric authentication system obtained a maximum accuracy of 90.8%, maximum genuine accept rate (GAR) of 0.936, minimum false acceptance rate (FAR) of 0.588, minimum false rejection rate (FRR) of 0.556 and maximum reputation score of 9.346.

Voice Recognition Based on Machine Learning Classification Algorithms: A Review

One essential component of biometric identity is voice recognition technology, which uses speech pattern analysis to authenticate people. With an emphasis on machine learning classification techniques, this review article thoroughly examines the field of speech recognition. We examine the effectiveness of random forest (RF), multilayer perceptrons (MLP), k-nearest neighbours (KNN), and support vector machine (SVM) classifiers via painstaking analysis and empirical evaluation. Utilizing a collection of Sepedi speech audio files, our results demonstrate the remarkable accuracy of 99.86% that RF is capable of producing. Aside from visual aids for better understanding, assessment indicators like as accuracy, precision, recall, F-measure, and root mean square error (RMSE) clarify the effectiveness of the model. The research highlights how machine learning algorithms, especially reinforcement learning (RF), have the capacity to revolutionize speech recognition technology in a variety of contexts.

Advancing Biometric Identity Recognition with Optimized Deep Convolutional Neural Networks

Advancing Biometric Identity Recognition with Optimized Deep Convolutional Neural Networks

Priority-based Multi-feature Vector Model Using Convolution Neural Network for Biometric Authentication

Many societal institutions have increased standards for the efficacy and dependability of identification systems in response to the ever-increasing sophistication of computer technology. Biometric identity systems have mostly supplanted the usage of conventional key and smart card systems in highly secure industries. There has been a dramatic increase in the adoption of biometric authentication systems in the last decade. While many biometric systems have relied on exterior physiological traits like fingerprints, iris scans, palm prints, and faces, very few have investigated the possibility of using interior physiological traits as a biometric. It is challenging for the benefits of unimodal biometric technology to be realized in real-world applications because to its inherent constraints, such as the fact that it only proposes a single piece of information and that environmental factors can impact data verification. While passwords have served their purpose of authenticating users and controlling access, they have also revealed their weaknesses. Concerns about forging or impersonating authentication have prompted the proposal of various Artificial Intelligence (AI)-based solutions. A lot of people are interested in user authentication based on ECGs these days. Unlike other biometrics, an electrocardiogram verifies that the person is actual and alive, making it one of the most trustworthy advanced authentication methods. The P, Q, R, S, and T characteristic points of an ECG signal are responsible for its most salient features. Feature selection is a statistical method that can be implemented in a supervised or unsupervised setting via regression or classification. A Convolution Neural Network (CNN) is a type of neural network that extracts and feeds into another neural network, which then classifies those extracted features. The ECG signal serves as input to a feature extraction network. The neural network performs categorization based on the retrieved feature signals. To overcome the problem that the recognition accuracy of traditional ECG identification methods declines with the rise in the number of testing samples at various moments or throughout different heartbeat cycles, this research offers a multi feature reuse model. In this research, an Associated Priority-based Weighted Multi-Feature Vector model using Convolution Neural Network (APbWMFV-CNN) is proposed for ECG signal-based authentication. The proposed model selects the most appropriate features used for accurate biometric model. The comparison results represent that the proposed model performance in user detection is high.

Robust biometric identity authentication scheme using quantum voice encryption and quantum secure direct communications for cybersecurity

A significant problem facing cybersecurity is that most client/server identity authentication schemes depend on computational complexity which is recently challenged by the super exponential computational abilities of quantum computers. Quantum cryptography with its quantum mechanics principles solves this problem and provides mathematically un-hackable secure communication for the quantum computers epoch. Biometric authentication has a big challenge related to its protection as it has to be submitted and stored in encrypted form. Unfortunately, most existing biometric cryptosystems rely on classical cryptography, so they are insecure in quantum era. This problem is solved in this paper by introducing a novel three-factor biometric quantum identity authentication scheme combining something you know (password), something you produce (client’s voice), and something you are (client’s face image) is proposed. The scheme consists of two phases: client enrollment and client login/authentication. The methodology is to use in the enrollment phase, two layers of quantum encryption for the client’s voice signal. Each layer consists of two simple quantum circuits of SWAP and C-NOT quantum gates respectively using a different pseudorandom key obtained from a cellular automata (CA) pseudorandom generator. The proposed scheme has many merits. Firstly, it introduces a new design of muti-factors quantum biometric authentication scheme as the biometric voice data is processed, transmitted, and stored at the server database in an encrypted unconditionally secure quantum form which provides parallel processing, real-time communications, inherent level of security against attacks, tamper-proof security and exponential powerful storage capability due to the quantum properties. Secondly, the proposed scheme achieves double-layers security by combing quantum voice encryption and quantum secure direct communication in one process such that the quantum channel eavesdropper cannot obtain any valuable information in case of information leakage. Thirdly, the distribution and management of encryption key problem is solved as there is no need for key exchange prior to communication. Fourthly, it achieves high communication efficiency as although it is a three-factor biometric identity authentication, only one biometric data is sent in quantum encrypted form to the server. Fifthly, the numerical simulations showed that the proposed scheme has large key space, high key sensitivity and more robust performance against different types of attacks compared to other existing classical and quantum voice encryption schemes. Finally, as an identity authentication method, the proposed scheme achieves identification accuracy of 100% with zero False Rejection Rate (FRR) and zero False acceptance rate (FAR) which is perfect.

Deep Learning Based Lipreading for Video Captioning

Visual speech recognition, often referred to as lipreading, has garnered significant attention in recent years due to its potential applications in various fields such as human-computer interaction, accessibility technology, and biometric security systems. This paper explores the challenges and advancements in the field of lipreading, which involves deciphering speech from visual cues, primarily movements of the lips, tongue, and teeth. Despite being an essential aspect of human communication, lipreading presents inherent difficulties, especially in noisy environments or when contextual information is limited. The McGurk effect, where conflicting audio and visual cues lead to perceptual illusions, highlights the complexity of lipreading. Human lipreading performance varies widely, with hearing-impaired individuals achieving relatively low accuracy rates. Automating lipreading using machine learning techniques has emerged as a promising solution, with potential applications ranging from silent dictation in public spaces to biometric authentication systems. Visual speech recognition methods can be broadly categorized into those that focus on mimicking words and those that model visemes, visually distinguishable phonemes. While word-based approaches are suitable for isolated word recognition, viseme-based techniques are better suited for continuous speech recognition tasks. This study proposes a novel deep learning architecture for lipreading, leveraging Conv3D layers for spatiotemporal feature extraction and bidirectional LSTM layers for sequence modelling. The proposed model demonstrates significant improvements in lipreading accuracy, outperforming traditional methods on benchmark datasets. The practical implications of automated lipreading extend beyond accessibility technology to include biometric identity verification, security surveillance, and enhanced communication aids for individuals with hearing impairments. This paper provides insights into the advancements, challenges, and future directions of visual speech recognition research, paving the way for innovative applications in diverse domains.

Biometric identity recognition based on contrastive positive-unlabeled learning

With the development of neural networks, identity recognition techniques utilizing human physiological signals have found applications in healthcare systems. However, many current physiological signal datasets have to spend a lot of resources in the pre-labeling phase, which leads to longer training times. To reduce the cost of labeling, we propose a self-supervised time-series representation learning framework based on contrastive positive-unlabeled learning (TS − CPUL), to learn the representations of temporal neighborhood sequences. To obtain the representations, the unit root test divides the unlabeled time series into neighborhood samples and unlabeled samples. Secondly, we use neighborhood sample representations and unlabeled sample representations to do self-supervised contrastive learning and positive-unlabeled learning with a negative sample selector. Both approaches can deepen the model’s understanding of the sample representation. Lastly, to enhance the model’s capability in discriminating between ambiguous sample pairs, we design the distance-polarized matrix. The proposed TS-CPUL is evaluated through experiments on three physiological signal datasets. The results demonstrate its effectiveness in understanding time-series representations and performing well in classification. For the identity recognition task, we achieve accuracy comparable to the supervised model by fine-tuning the model with only 10% of the labeled data.

Privacy-Preserving Decentralized Biometric Identity Verification in Car-Sharing System

Privacy-Preserving Decentralized Biometric Identity Verification in Car-Sharing System

Electrocardiogram identification based on data generative network and non-fiducial data processing

Nowadays, the use of biological signals as a criterion for identity recognition has gained increasing attention from various organizations and companies. Therefore, it has become crucial to have a biometric identity recognition method that is fast and accurate. In this paper, we propose a linear electrocardiogram (ECG) data preprocessing algorithm based on Kalman filters for rapid noise data filtering (wavelet transform filtering algorithm). Additionally, we introduce a generative network model called Data Generation Strategy Network (DRCN) based on generative networks. The DRCN is employed to augment training samples for convolutional classification networks, ultimately improving the classification performance of the model. Through the final experiments, our method successfully reduced the average misidentification rate of ECG-based identity recognition to 2.5%, and achieved an average recognition rate of 98.7% for each category, significantly surpassing previous achievements. In the future, this method is expected to be widely applied in the field of ECG-based identity recognition.

Working against the clock: digital surveillance in US Medicaid homecare services

This article explores the implementation of a digital verification system known as Electronic Visit Verification (EVV) across homecare services for older and disabled adults within the US Medicaid program. EVV systems are used to conduct daily check-ins through GPS tracking and biometric identity verification. While touted as a means to identify and deter “fraud, waste, and abuse,” the digital monitoring also generates detailed data trails on the lives and habits of service recipients, as well as constraining their daily movements. Drawing on qualitative interviews with workers and clients, I argue that this case study calls attention to how harms from digitalization of social welfare provision emerge from workplace surveillance and labor management, and how EVV becomes a tool for more finely tuning classifications of different types of paid and unpaid care. The burdensome digital compliance hurdles reinforced older employment tensions between the state, care workers, and public benefits recipients.

Courts, Capacity, and Facts: The Constitutional Review of Biometric ID Systems

Courts, Capacity, and Facts: The Constitutional Review of Biometric ID Systems

Revolutionizing Healthcare: A Comprehensive Framework for Personalized IoT and Cloud Computing-Driven Healthcare Services with Smart Biometric Identity Management

Medical care conveyance has been transformed by the Internet of Things (IoT's) combination into wellbeing systems, which provides doctors and patients with continuous on-request services. However, this coordination poses questions with respect to the precision of the information and possible security risks. This research expects to present a sharp character the executives structure planned for IoT and distributed computing based personalized medical care frameworks. The purpose is to upgrade confirmation processes while restricting security threats through the double-dealing of multimodal encoded biometric features. The suggested approach incorporates biometric-based continuous authentication together with combined and concentrated personality access strategies. To safeguard patient information in the cloud, it combines electrocardiogram (ECG) and photoplethysmogram (PPG) signals for authentication, which is further bolstered by homomorphic encryption (HE). An AI (ML) model was used to assess the system's reasonability including a dataset of 20 clients in various seating configurations. The merged based biometric structure defeated standalone ECG or PPG signal-based procedures in perceiving and authenticating every client with 100% exactness. The proposed framework makes significant improvements to the privacy and security of personalized healthcare frameworks. It fulfills the essential security necessities and is by the by viable enough to run on low-end processors. It guarantees trustworthy authentication and protects against conventional security threats by utilizing multimodal biometric features and cutting-edge encryption techniques.

EEG Topography Amplification Using FastGAN-ASP Method

Electroencephalogram (EEG) signals are bioelectrical activities generated by the central nervous system. As a unique information factor, they are correlated with the genetic information of the subjects, exhibiting robustness against forgery. The development of biometric identity recognition based on EEG signals has significantly improved the security and accuracy of biometric recognition. However, EEG signals obtained from incompatible acquisition devices have low universality and are prone to noise, making them challenging for direct use in practical identity recognition scenarios. Employing deep learning network models for data augmentation can address the issue of data scarcity. Yet, the time–frequency–space characteristics of EEG signals pose challenges for extracting features and efficiently generating data with deep learning models. To tackle these challenges, this paper proposes a data generation method based on channel attention normalization and spatial pyramid in a generative adversative network (FastGAN-ASP). The method introduces attention mechanisms in both the generator and discriminator to locate crucial feature information, enhancing the training performance of the generative model for EEG data augmentation. The EEG data used here are preprocessed EEG topographic maps, effectively representing the spatial characteristics of EEG data. Experiments were conducted using the BCI Competition IV-Ⅰ and BCI Competition IV-2b standard datasets. Quantitative and usability evaluations were performed using the Fréchet inception distance (FID) metric and ResNet-18 classification network, validating the quality and usability of the generated data from both theoretical and applied perspectives. The FID metric confirmed that FastGAN-ASP outperforms FastGAN, WGAN-GP, and WGAN-GP-ASP in terms of performance. Moreover, utilizing the dataset augmented with this method for classification recognition achieved an accuracy of 95.47% and 92.43%.

Blockchain-based biometric identity management

In recent years, face biometrics recognition systems are a wide space of a computer usage which is mostly employed for security purpose. The main purpose of the face biometrics recognition system is to authenticate a user from a given database. Due to the widespread expansion of the surveillance cameras and facial recognition technology, a robust face recognition system required. The recognition system needs to store a large number of training samples in any storage unit, that time hackers can access and control that data. So, Protecting and managing sensitive data is essential object. This requires a technique that preserve the privacy of individuals, maintain data integrity, and prevent information leakage. The storage of biometric templates on centralized servers has been associated with potential privacy risks. To address this issue, we have developed and implemented a proof-of-concept facial biometric identification system that uses a private Blockchain platform and smart contract technology. So, the proposed approach is presented a secure and tamper-proof from data breaches as well as hacks with data availability, by using the Blockchain platform to store face images. This paper aims to utilize Blockchain technology to identify individuals based on their biometric traits, specifically facial recognition system makes it tamper-proof (immutable) ensuring security. The system consists of enrolment and authentication phases. Blockchain technology uses peer-to-peer communication, cryptography, consensus processes, and smart contracts to ensure the security. The proposed approach was tested on two popular datasets: CelebFaces Attributes (CelebA) and large-scale face UTKFace datasets. The experimental results indicate that the system yields highly performance outcomes, as evidenced by the Equal Error Rate (EER) values of 0.05% and 0.07% obtained for the CelebA and UTKFace datasets, respectively. The system was compared to three baseline methods and scored the lowest Equal Error Rate.

Thermal image identification against pose and expression variations using deep learning

Thermal infrared face recognition systems have developed as an effective complement to visual systems for biometric identity purposes and military operations. With the help of deep learning and convolutional neural networks, we provide an efficient approach for thermal facial identification that can handle position fluctuations and expression dissimilarities in this study. As an essential deep learning model that has demonstrated its effectiveness in several computer vision and machine learning applications, we employ the ResNet-50 architecture which consists of 50 layers of convolution, activation, and pooling. The structures of those layers are discussed in detail to gain a profound insight about the operation of this architecture. In this regard, a deep and detailed mathematical analysis is furnished. The system is implemented on a dataset of 1500 thermal images, where we execute experiments in various setups and circumstances to address the issues with posture and expression variance. The experimental results show that the system achieves an accuracy rate of 99.4% when it is trained using 30% of the dataset after five epochs. With respect to other performance measures, the system attains 100% recall, precision, F-score, and specificity. In comparison to recently published works, the findings show that the suggested system offers improved discriminability, resilience against fluctuations, as well as high identification rates under diverse settings that mimic real-world scenarios.

Comparison of the Ability of Neural Network Model and Humans to Detect a Cloned Voice

The vulnerability of the speaker identity verification system to attacks using voice cloning was examined. The research project assumed creating a model for verifying the speaker’s identity based on voice biometrics and then testing its resistance to potential attacks using voice cloning. The Deep Speaker Neural Speaker Embedding System was trained, and the Real-Time Voice Cloning system was employed based on the SV2TTS, Tacotron, WaveRNN, and GE2E neural networks. The results of attacks using voice cloning were analyzed and discussed in the context of a subjective assessment of cloned voice fidelity. Subjective test results and attempts to authenticate speakers proved that the tested biometric identity verification system might resist voice cloning attacks even if humans cannot distinguish cloned samples from original ones.

A Framework for Human-Algorithm Teaming in Biometric Identity Workflows

Biometric identity workflows are made up of multiple subsystems which can be automated to varying degrees using computer algorithms. Setting the appropriate level of automation for each subsystem is crucial for optimal system performance, which relies on synergistic human-algorithm teams. In this work, we leverage an automation design framework from prior research to define levels of automation for each biometric subsystem. This framework aligns a four-stage model of human processing with equivalent system functions. We propose applying this framework as a method to determine the appropriate allocation of tasks between humans and algorithms within a biometric identity workflow. While previous work has focused on the role of the human in the comparison and decision subsystems, we emphasize the need to consider the full biometric system in determining the role of humans in biometric identity workflows.

Secure medical data storage in DPOS-hyper ledger fabric block chain using PM-ECC and L2-DWT

Governments and individuals have taken extraordinary measures to protect the health of the people during the COVID pandemic. Stored medical data remains the main target for hackers, and hence it needs to be stored securely. To achieve this objective, this paper proposes a novel model using Delegated Proof of Stake-Hyper ledger Fabric Block Chain (DPOS-HFBC). Primarily, by employing LL Subbandeigen Value decomposition employed Discrete Wavelet Transform (L2-DWT), the patient’s Lung Computed Tomography (CT) image data are collected and embedded. For embedding, the patient’s name and ID are taken. In embedding, a Pseudorandom number generator using the Mersenne twister algorithm employed in Elliptic Curve Cryptography (PM-ECC) is applied for key encryption. It covered the image that was embedded with the original and then stored in DPOS-HFBC. Likewise, for authorization, every patient’s biometric ID was hashed and stored in DPOS-HFBC. Data requesters request data in the Interplanetary File System (IPFS) of DPOS-HFBC, and the attributes from the request are extracted and sent to the authority for verification. After verifying, the authority shares their biometric ID with the requester and this gets hashed and then verified in DPOS-HFBC. To show the model’s supremacy, the proposed method was evaluated and compared with existing methods.