Biometric Identity Research Articles

Deep Neural Network for Complex Human Activity Recognition

The problem statement for our project is to recognize the automatic emotions from the analysis of body movement. It has tremendous potential to bring in some changes into domains like robotics, biometric identity recognition etc. A computer can identify human emotions from the facial expressions; this may bring about changes in the way we interact with the computer. One of the challenges is to identify emotion specific features from a vast number of descriptors of human body. In this project we used feature selection frame work to accurately recognize basic emotions namely happiness, sadness, anger, depression and neutral. This project consists of two layers, the first layer, a unique combination of Analysis of variance (ANOVA) and multivariate Analysis of variance (MANOVA), to eliminate irrelevant features. The proposed model can detect complex actions like sitting, walking and few others. The proposed model achieved recognition accuracy of 90.0% during walking, 96.0% during sitting, demonstrating high accuracy and robustness of the developed method.

People as data, data as oil: the digital sovereignty of the Indian state

ABSTRACT This article connects the Indian state’s regulation of the digital economy, exemplified by the 2019 draft of the e-commerce policy, to its regulation and control of bodies, specifically through the biometric ID, Aadhaar, and its proliferating uses. It argues that the thrust of India’s emergent project of digital sovereignty is not merely geopolitical, but also biopolitical, a process through which the Indian state is engaged in altering what it is to be sovereign and its subject.

Improvement of Eye Tracking Based on Deep Learning Model for General Purpose Applications

The interest in the Eye-tracking technology field dramatically grew up in the last two decades for different purposes and applications like keeping the focus of where the person is looking, how his pupils and irises are reacting for a variety of actions, etc. The resulted data can deliver an extraordinary amount of information about the user when it's interlocked through advanced data analysis systems, it may show information concerned with the user’s age, gender, biometric identity, interests, etc. This paper is concerned about eye motion tracking as an unadulterated tool for different applications in any field required. The improvements in this area of artificial intelligence (AI), machine learning (ML), and deep learning (DL) with eye-tracking techniques allow large opportunities to develop algorithms and applications. In this paper number of models were proposed based on Convolutional neural network (CNN) have been designed, and then the most powerful and accurate model was chosen. The dataset used for the training process (for 16 screen points) consists of 2800 training images and 800 test images (with an average of 175 training images and 50 test images for each spot on the screen of the 16 spots), and it can be collected by the user of any application based on this model. The highest accuracy achieved by the best model was (91.25%) and the minimum loss was (0.23%). The best model consists of (11) layers (4 convolutions, 4 Max pooling, and 3 Dense). Python 3.7 was used to implement the algorithms, KERAS framework for the deep learning algorithms, Visual studio code as an Integrated Development Environment (IDE), and Anaconda navigator for downloading the different libraries. The model was trained with data that can be gathered using cameras of laptops or PCs and without the necessity of special and expensive equipment, also It can be trained for any single eye, depending on application requirements.

“Covid-19 Vaccines” for Children in the UK: A Tale of Establishment Corruption

How and why has it come to pass that children as young as 12 in the UK are being injected with a novel form of mRNA technology that is unlicensed, has no long-term safety data, and remains in clinical trials until May 2023? This article traces the path by which the unthinkable became an alarming reality between October 2020 and September 2021 and also follows developments since then. Working chronologically, the actions and claims of the manufacturers, the regulators, politicians, and in particular the establishment media in promoting “COVID-19 vaccination” for children are examined. The actions taken by policy makers are juxtaposed to scientific evidence available showing that there has never been any rational justification for the mass rollout of “COVID-19 vaccines” to children. The rollout has been predicated on shifting narratives, obfuscations, faux justifications, outright lies, regulatory capture of supposed guardians of the public interest, and mass propaganda. Evidence of actual and potential injuries to children has accumulated from before the beginning of the rollout, in spite of repeated attempts to cover it up, and yet, the under-12s are now also in the crosshairs and children are being targeted for “booster shots.” A clear picture emerges of collusion and corruption at the highest levels in forcing through an agenda that runs contrary to public health, democracy, and freedom. It is becoming clear that the rollout to children has nothing to do with “SARS-CoV-2” and everything to do with ongoing efforts to refashion the international monetary system in the image of central bank digital currencies and biometric IDs. In pursuit of that agenda, the transnational ruling class has revealed that it is willing to maim and kill children knowingly, creating enormous potential for a backlash as the public becomes aware of what is being done.

Multi-Factor Authentication for Secured Financial Transactions in Cloud Environment

The rise of the digital economy and the comfort of accessing by way of user mobile devices expedite human endeavors in financial transactions over the Virtual Private Network (VPN) backbone. This prominent application of VPN evades the hurdles involved in physical money exchange. The VPN acts as a gateway for the authorized user in accessing the banking server to provide mutual authentication between the user and the server. The security in the cloud authentication server remains vulnerable to the results of threat in JP Morgan Data breach in 2014, Capital One Data Breach in 2019, and many more cloud server attacks over and over again. These attacks necessitate the demand for a strong framework for authentication to secure from any class of threat. This research paper, propose a framework with a base of Elliptical Curve Cryptography (ECC) to perform secure financial transactions through Virtual Private Network (VPN) by implementing strong Multi-Factor Authentication (MFA) using authentication credentials and biometric identity. The research results prove that the proposed model is to be an ideal scheme for real-time implementation. The security analysis reports that the proposed model exhibits high level of security with a minimal response time of 12 s on an average of 1000 users.

Mistrust of government within authoritarian states hindering user acceptance and adoption of digital IDs in Africa: The Nigerian context

Abstract Nigeria commenced its national foundational digital identity project in 2007 and had enrolled 60 million people by July 2021. The project, led by the National Identity Management Commission (NIMC), seeks to unify the country’s public and private functional identity databases, and aims to improve government services and national security. Although the enrolment process had encountered initial challenges such as the absence of enrolment centers in some communities across the country, enrolment for the biometric ID had proceeded without any significant public objection to its objectives. Following the EndSARS protests of October 2020, where youths protesting police violence and perceived poor governance were shot at by government security forces and protesters placed under surveillance, the government announced an updated national identity policy mandating citizens link their National Identity Number (NIN) with their SIM card information. For the first time, significant pockets of resistance arose against the national ID project by sections of the public who perceived the EndSARS violence as signaling a change in government behavior, and the updated ID policy as a mechanism for empowering government surveillance and authoritarianism. The resistance to the ID project marked a shift in public perception which threatens its future. This paper argues that mistrust in government data collection projects grows when data collection is perceived to be increasing government power to the detriment of human rights and freedom. It also puts forward a proposal on how to restore trust within the low-trust environment in Nigeria including the passage of a data protection law and amendments to the NIMC Act and Policies/Regulations, establishing Federated identity providers which give choices to end-users, and delinking the NIN from functional identity databases.

Selective Cancellable Multi-Biometric Template Generation Scheme Based on Multi-Exposure Feature Fusion

This article introduces a new cancellable multi-biometric system based on the combination of a selective encryption method and a deep-learning-based fusion technology. The biometric face image is treated with an automatic face segmentation algorithm (Viola-Jones), and the image of the selected eye is XORed with a PRNG (Pseudo Random Number Generator) matrix. The output array is used to create a primary biometric template. This process changes the histogram of the selected eye image. Arnold’s Cat Map is used to superimpose the PRN pixels only on the pixels of the primary image. Arnold’s cat map deformed eyes are encrypted using the Advanced Encryption Standard (AES) to encrypt the biometric data stored in the database. In addition, the AES master key is used for the same person in the identity verification process to verify the biometric identity. It is created from the fingers of the right hand, and the right eye is integrated into this process using deep learning technology. The deep learning fusion process can prevent attacks on the biometric system as a whole. In order to avoid damage to the eye or fingerprint images, the design considers the other eye and fingerprint images.

Fingerprint bio‐key generation based on a deep neural network

With the increasing use of biometric identity authentication, biological key generation technology is receiving much attention. A high-strength key that is easy to store and manage can be generated from biological characteristics, which can improve the convenience and security of user-encryption operations. However, the generation of a high-strength, stable, and robust key using the currently available fingerprint bio-key generation technology is difficult. This paper proposes a three-layer framework for fingerprint bio-key generation that is composed of a fingerprint bio-key preprocessor, fingerprint bio-key stabilizer (FPBK_Stabilizer), and fingerprint bio-key fuzzy extractor. In the FPBK_Stabilizer, feature selection and layer-by-layer convolution projection characteristics from deep neural networks are used to effectively eliminate the instability between fingerprint samples. Furthermore, a suitable multilayer convolutional projection fingerprint bio-key generation model is designed for generating the fingerprint bio-key. The results of a fingerprint bio-key generation experiment involving a fingerprint library comprising 100 people verified the efficacy of the proposed framework. Specifically, the proposed framework exhibited a generation intensity >1024 bits, accuracy rate >98.0%, and misrecognition rate <1.5%, thereby verifying its high-strength, stable, and robust fingerprint bio-key generation capability.

Mathematical thinking about systems – students modeling a biometrics identity verification system

ABSTRACT The aim of this study is to understand how students’ mathematical thinking is activated and nurtured in solving a modeling problem, where the problem situation involves the design of a system. From a STEM integrated perspective, 9th grade students worked on a modeling task aiming to create an identification system based on hand biometrics. The theoretical framework proposes a conceptualization of the interplay between the mathematical modeling process, from a cognitive perspective, and the engineering design process. Central ideas refer to the cyclical nature of both processes and to the sub-processes involved in them. The empirical data were collected in two design-based research cycles with different 9th grade classes. The data from the groups’ audio and video recording and the students’ productions were analyzed under a directed qualitative content analysis informed by theory. The results showed a global pattern in the students’ thinking in solving a design system problem. The overlapping and interplay between the mathematical modeling and the design process was a prominent characteristic of students’ thinking. The modeling cycle was mirrored by a design cycle, with both running in parallel. System thinking pushed and drove students’ mathematical thinking, from the system requirements to the prototype validation.

Biometrics and the metaphysics of personal identity

AbstractThe vast advances in biometrics over the past several decades have brought with them a host of pressing concerns. Philosophical scrutiny has already been devoted to many of the relevant ethical and political issues, especially ones arising from matters of privacy, bias, and security in data collection. But philosophers have devoted surprisingly little attention to the relevant metaphysical issues, in particular, ones concerning matters of personal identity. This paper aims to take some initial steps to correct this oversight. After discussing the philosophical problem of personal identity, the ways in which the notion of biometric identity connects with, or fails to connect with, the philosophical notion of personal identity is explored. Though there may be some good reasons to use biometric identity to track personal identity, it is contended that biometric identity is not the same thing as personal identity and thus that biometrics researchers should stop talking as if it were.

Self-restrained triplet loss for accurate masked face recognition

Using the face as a biometric identity trait is motivated by the contactless nature of the capture process and the high accuracy of the recognition algorithms. After the current COVID-19 pandemic, wearing a face mask has been imposed in public places to keep the pandemic under control. However, face occlusion due to wearing a mask presents an emerging challenge for face recognition systems. In this paper, we present a solution to improve masked face recognition performance. Specifically, we propose the Embedding Unmasking Model (EUM) operated on top of existing face recognition models. We also propose a novel loss function, the Self-restrained Triplet (SRT), which enabled the EUM to produce embeddings similar to these of unmasked faces of the same identities. The achieved evaluation results on three face recognition models, two real masked datasets, and two synthetically generated masked face datasets proved that our proposed approach significantly improves the performance in most experimental settings.

BODIES AND DATA: THE DIGITAL SOVEREIGNTY OF THE INDIAN STATE

In February 2019, the Government of India released a draft e-commerce policy that boldly proclaimed, “India and its citizens have a sovereign right to their data,” (Department for Promotion of Industry and Internal Trade, 2019) a right that could not extend to non-Indians. The draft policy was one of many in recent years that have mandated data localization, protectionism or other measures of greater national control over the digital economy in India. These actions are widely read as a nation-state asserting its sovereignty in the digital realm against the power of US-based global tech firms. They are sold in geopolitical terms, a digital sovereignty that challenges digital colonialism. I argue that the Indian state’s project of digital sovereignty must also be understood as biopolitical. I connect the Indian state’s regulation of the digital economy, exemplified by the 2019 draft of the e-commerce policy to its regulation and control of bodies, specifically through the biometric ID, Aadhaar, and its proliferating uses. I read these state actions collectively as a process through which the Indian state is engaged in altering what it is to be sovereign and its subject. When “digital” appends sovereignty, it is not merely a new terrain upon which to exert power, nor is it an unbridled force that the state must contend with, it is the means through which the Indian state enacts its project(s) of domination.

Seeded Ising Model and Distributed Biometric Template Storage and Matching.

It is known that a variant of Ising model, called Seeded Ising Model, can be used to recover the information content of a biometric template from a fraction of information therein. The method consists in reconstructing the whole template, which is called the intruder template in this paper, using only a small portion of the given template, a partial template. This reconstruction method may pose a security threat to the integrity of a biometric identity management system. In this paper, based on the Seeded Ising Model, we present a systematic analysis of the possible security breach and its probability of accepting the intruder templates as genuine. Detailed statistical experiments on the intruder match rate are also conducted under various scenarios. In particular, we study (1) how best a template is divided into several small pieces called partial templates, each of which is to be stored in a separate silo; (2) how to do the matching by comparing partial templates in the locked-up silos, and letting only the results of these intra-silo comparisons be sent to the central tallying server for final scoring without requiring the whole templates in one location at any time.

Biometric identity systems in law enforcement and the politics of (voice) recognition: The case of SiiP

Biometric identity systems are now a prominent feature of contemporary law enforcement, including in Europe. Often advanced on the premise of efficiency and accuracy, they have also been the subject of significant controversy. Much attention has focussed on longer-standing biometric data collection, such as finger-printing and facial recognition, foregrounding concerns with the impact such technologies can have on the nature of policing and fundamental human rights. Less researched is the growing use of voice recognition in law enforcement. This paper examines the case of the recent Speaker Identification Integrated Project, a European wide initiative to create the first international and interoperable database of voice biometrics, now the third largest biometric database at Interpol. Drawing on Freedom of Information requests, interviews and public documentation, we outline the emergence and features of SiiP and explore how voice is recognised and attributed meaning. We understand Speaker Identification Integrated Project as constituting a particular ‘regime of recognition’ premised on the use of soft biometrics (age, language, accent and gender) to disembed voice in order to optimise for difference. This, in turn, has implications for the nature and scope of law enforcement, people's position in society, and justice concerns more broadly.

High-security photoacoustic identity recognition by capturing hierarchical vascular structure of finger.

Currently, most biometric methods mainly use single features, making them easily forged and cracked. In this study, a novel triple-layers biometric recognition method, based on photoacoustic microscopy, is proposed to improve the security of biometric identity recognition. Using the photoacoustic (PA) dermoscope, three-dimensional absorption-structure information of the fingers was obtained. Then, by combining U-Net, Gabor filtering, wavelet analysis and morphological transform, a lightweight algorithm called photoacoustic depth feature recognition algorithm (PADFR) was developed to automatically realize stratification (the fingerprint, blood vessel fingerprint and venous vascular), extracting feature points and identity recognition. The experimental results show that PADFR can automatically recognize the PA hierarchical features with an average accuracy equal to 92.99%. The proposed method is expected to be widely used in biometric identification system due to its high security.

Building a trusted guardian for our biometric identities

These days, unlocking a phone with a quick glance or the touch of a finger – rather than a passcode – has become so popular that we almost forget that we are using our biometric information to log in. The same can be said for authenticating payments or signing up to the latest challenger bank. In return for convenience, and to prove we are who we say we are, most of us now happily allow commercial companies and other organisations to process our biometric facial, fingerprint, iris or voice print data.

A Tokenless Cancellable Scheme for Multimodal Biometric Systems

Biometric template protection (BTP) is an open problem for biometric identity management systems. Cancellable biometrics is commonly designed to protect biometric templates with two input factors i.e., biometrics and a token used in template replacement. However, the token is often required to be kept secretly; otherwise, the protected template could be vulnerable to several security attacks and breaches of privacy. In this paper, we propose a tokenless cancellable biometrics scheme called Multimodal Extended Feature Vector (M∙EFV) Hashing that employs an improved XOR encryption/decryption notion to operate on the transformation key. We stress on multimodal biometrics where the real-valued face and fingerprint vectors are fused and embedded into a binarized cancellable template. Specifically, M∙EFV hashing consists of three stages of transformation: 1) normalization and biometric fusion; 2) randomization and binarization; and 3) cancellable template generation. To evaluate the proposed scheme, several benchmarking datasets, i.e., FVC2002, FVC2004 for fingerprint and LFW for face are used in experiments. The verification performance is validated by employing the FVC matching protocol. Various attacks are simulated and analysed in the worst-case scenario. Lastly, unlinkability and revocability properties are examined experimentally.

Connective action or collective inertia? Emotion, cognition, and the limits of digitally networked resistance

ABSTRACT Connective action, or individuals networking with each other online to form social movements, rarely leads to lasting change. In this study, we argue that such movements are ultimately ineffective because they struggle to sustain themselves over time and identify the reasons behind their transience. Our analysis focuses on Twitter conversations about Aadhaar, a biometric ID project that has raised concerns about data privacy and civil liberty in India, the world’s largest democracy. A computational mixed-methods approach incorporating social network analysis, sentiment analysis, and structural topic modeling demonstrates that connective action against Aadhaar failed to produce a sustained discourse of resistance, with people’s feelings toward and beliefs about Aadhaar vacillating sharply. The analysis draws attention to the power of brick-and-mortar social institutions, including the state and its agencies, political parties, courts, technology companies, and ‘legacy’ news media, in shaping and reshaping seemingly bottom-up discourses on digital platforms. It also identifies three interlinked weaknesses of connective action – the individualized nature of mobilization, excessive flexibility of social networks, and a negative emotional culture. We contend that in order to be effective, contemporary social movements need to utilize digital technologies for ‘collective’ action by forging collective identities that bind participants affectively and cognitively, empower them against structures of social control, and enable them to commit to non-personal and long-term objectives.

The law in computation: What machine learning, artificial intelligence, and big data mean for law and society scholarship

AbstractComputational systems, including machine learning, artificial intelligence, and big data analytics, are not only inescapable parts of social life but are also reshaping the contours of law and legal practice. We propose turning more law and social science (LSS) attention to new technological developments through the study of “law in computation,” that is, computational systems' integration with regulatory and administrative procedures, the sociotechnical infrastructures that support them, and their impact on how individuals and populations are interpellated through the law. We present a range of cases in three areas of inquiry ‐ algorithmic governance, jurisdiction and agency ‐ on issues such as immigration enforcement, data sovereignty, algorithmic warfare, biometric identity regimes, and gig economies, for which examining law in computation illuminates how new technological systems' integration with legal processes pushes the distinction between “law on the books” and “law in action” into new domains.We then propose future directions and methods for research. As computational systems become ever more sophisticated, understanding the law in computation is critical not only for LSS scholarship, but also for everyday civics.

Becoming data: biometric IDs and the individual in ‘Digital India’

AbstractAadhaar (literally ‘foundation’) is the largest national biometric identification drive the world has witnessed. An Aadhaar is a twelve‐digit ID number linked to its holder's iris scans, fingerprints, facial photograph, and demographic information in a centralized database. In but a decade, India has expeditiously enrolled over 90 per cent of its billion‐strong population into a Central Identities Data Repository. This essay is an ethnographic consideration of the processes by which Aadhaar enrollees become data, focusing on the sociopolitical valence of biometric data. It argues that the datafication of the body via Aadhaar occasions re‐examinations of – and contestations over – the idea of the individual in postcolonial India, a country often deemed sociocentric in popular and scholarly discourse alike. Further, it suggests that biometric socialization facilitates belonging in a ‘Digital India’, often rendered as a data cosmopolis in emergent technocratic imaginations.