Biometric Security Research Articles

Should social media users pay extra for biometric security?

Dan McLoughlin of OneSpan says recent moves by Twitter and Facebook could be the start of a trend to charge social media users more to secure their personal data. But can this be justified?

Guardians of Privacy: Understanding the European Union's Framework for Biometric Data Protection

With the growing prevalence of biometric technology, individual traits are being stored in an increasing number of databases. Users must maintain a high degree of awareness regarding biometric data security. It's important not to provide biometric details without thorough consideration of its necessity, scrutinizing the existing security measures, and understanding the track record of any entity requesting such information. The laws and regulations concerning this matter vary widely depending on the user's location. The European Union is renowned for its stringent personal data protection laws and robust security standards, imposing reporting responsibilities on any entity that collects and requests user data. This article will delve into the regulations and policies surrounding biometric data protection within the European Union.

Neural networks based face recognition system for biometric security

Neural networks based face recognition system for biometric security

Intelligent and assisted medicine dispensing machine for elderly visual impaired people with deep neural network fingerprint authentication system

Offering a medication to Elderly and Visually Impaired People (EVIP) in rural areas without staff assistance is a difficult task in the field of health care. The current pill dispenser has limitations when it comes to administering medication before and after food, doesn't take into account over- and under-dosing situations, and doesn't send notifications to the relevant medical entities when the weight of the pills falls below the threshold. Despite the availability of numerous techniques and methods for automating the delivery of pills, there is no assistive method for the timely and secure delivery of medication. The suggested architecture provides EVIP with five crucial capabilities that aren't present in any of the current pill dispensers in an integrated way. In this paper, we present a biometric sensor-based secure access method for medication kits. The right person is only allowed access to the kit based on the finger print scanner. Second, the suggested architecture provides a self-managed and patient-centric service through assisted and automatic medication delivery. The third feature of this paper is the design method for the efficient delivery of pills before and after meals. A method of alerting the medical entities whenever a medication is over or under dosed is a fourth feature of the system. As a fifth feature, the implementation also automatically notifies the pharmacist and doctor when the weight of the pill strips in the chamber falls below the threshold value. Through the use of an integrated Timely Assistance Messaging (TAM) algorithm, all five features are implemented in the suggested architecture. A finger print recognition system is included with VGG-16 deep neural network with image enhancement. The EVIP can use this proposed system in residences, medical facilities, and retirement communities.

Forensic Basics of Modern Biometric Technologies

Forensic identification and modern biometric technologies have a common basis and have been closely developing together in various ways. Today new biometric technologies have emerged that utilize previously inaccessible properties and characteristics of humans. The article examines how forensic identification principles are applied in the creation of such new technologies.Promising areas of collaboration between forensic experts and biometric technology specialists include collaborative studying the frequency and identification significance of external human characteristics, as well as individual features of movements and actions, determined by habits and skills and used for forensic identification. They share common goals such as investigating the causes of erroneous identifications, exploring the ways to illegally bypass biometric security systems, and developing methods to counter criminal attacks.

APPLICATIONS OF MULTIMODAL BIOMETRICS AUTHENTICATION FOR ENHANCING THE IOT SECURITY USING DEEP LEARNING

The Internet of Things (IoT) integrates billions of electronic devices into computer networks to provide advanced and intelligent services that enable devices to communicate with each other by exchanging information with minimal human interaction. The security issue is at higher risk in IoT systems than in other computing systems. Maintaining the security requirement when attacking the physical surface of the IoT system device is a challenging task. Implementing security mechanisms like authentication and access control for the IoT ecosystem is necessarily needed to ensure the security of IoT devices. The key used for security may be stolen, forgotten, or forged. Also, the key may be generated by intruders or men in the middle of traditional security mechanisms. Biometric security is becoming more advanced and sophisticated with technological advancements and is mostly used in authentication systems. In unimodal biometrics, only one biometrics character can be applied which does not apply to ensure the security of IoT systems. In this paper, Multimodal biometrics authentication was used for securing edge devices in the IoT ecosystems. Face image and fingerprint image were used as multimodal biometrics systems for authenticating users to secured IoT devices. A pi-camera module and fingerprint sensor were used to capture biometric data. Image processing techniques were then applied to the images. Then CNN algorithms were used for feature extraction and model creation. During model creation, the RELU function was used as an activation function, soft-max for image classification, and Max-pooling for image dimensional reduction which helped the model speed up the training process. Experimental results show that the accuracy of the face image and fingerprint image is 92% and 89% respectively, which is a promising result that achieves the objective of the study.
 Keywords: Internet of Things, Multimodal Biometrics, Authentication, CNN, Deep Learning

A novel cancelable finger vein templates based on LDM and RetinexGan

In this paper, we propose a new biometric template protection scheme, which can deal with the finger vein biometric security threats, through using the LDM and RetinexGAN model. The RetinexGAN model is mainly used to handle the illumination and low contrast problems effectively, while efficiently extracting discriminative features from the finger vein images. The projection of extracted features into dissimilarity space is done using Local Dissimilarity Map (LDM). LDM is an efficient way for finger vein features representation, which investigates the relationships and correlation inter and intra classes, while effectively coming up with the accidental shifts/rotations caused by the arbitrary position of the finger during image acquisition. The proposed approach is successfully evaluated in terms of non-invertibility, non-linkability, revocability and performances. Experimental results and comparison analysis with the state of arts methods confirm that the proposed framework can achieve promising results.

Audit Trails and Logging Biometric Authentication for Data Security in Banking Using Reporting Tools

Many sectors, including government, consumer electronics, and business security systems, are beginning to see the potential benefits of biometric authentication. More and more businesses, organisations, and individuals are turning to biometric security measures to keep cyberspace safe from malicious actors. Cybersecurity is the practice of protecting computer systems, networks, data, and software from intrusion and other forms of cyberattack. The provision of banking services through electronic means is known as "cyber banking." The rise of online banking is a reflection of the general shift in consumer spending habits. Despite its many advantages, online banking has been plagued by security breaches. Biometric security ensures the identity of individuals by confirming their behavioural and physical characteristics. It is the gold standard of physical security when it comes to verifying identities. Biometric authentication holds that individuals can be uniquely identified by their intrinsic physical or behavioural characteristics. To combat these difficulties, a plethora of security safeguards have been integrated across the entire online banking service. A major danger to the occurrence of criminal or terrorist behaviour, cybercrime has deep roots globally. These threats can jeopardise both internal and external security if no one in charge takes them seriously. Money and personal information are at risk if cybercrime goes undetected. Attacks on the Internet and its supporting infrastructure have happened before. Every single day, crimes involving computers occur, such as hacker attacks and online fraud. In order to combat the threat of cybersecurity and offer the banking industry with a high level of safety and security, the study delves into the essential characteristics of biometric systems in both conventional and Islamic banking

Enhancing security of biometrics based authentication framework for DRM system

Enhancing security of biometrics based authentication framework for DRM system

A Review of Homomorphic Encryption for Privacy-Preserving Biometrics.

The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward.

Smart Multi Verification Based Security System

In this technologically evolving era, security plays a significant role in preventing different assets and crimes. This inconsistency developed an innovative idea to improve the level and solve the existing problems. This paper proposes a well-suited multilayer security system for homes, bank lockers, and more locations we can use it. Traditionally, password and biometric double-layer security systems use everywhere, but this embedded solution combines RFID, OTP, and fingerprint identification in sequence. Essential modules are connected and controlled through a microcontroller with a GSM module. Every operation done by the system pushes to the IoT cloud, and the mobile application shows the status of every action. The right authorized access can let the magnetic switch open, and every unauthorized access turns on the alarm with appropriate message notifications. The Proposed system is more effective and reliable due to multistage security, and it is not easy to crack with the combination of all three stages. The whole system's workings indicate by led flashes. This implementation has shown better results and a higher performance rate than existing methods

Deep Contrastive Learning-Based Model for ECG Biometrics

The electrocardiogram (ECG) signal is shown to be promising as a biometric. To this end, it has been demonstrated that the analysis of ECG signals can be considered as a good solution for increasing the biometric security levels. This can be mainly due to its inherent robustness against presentation attacks. In this work, we present a deep contrastive learning-based system for ECG biometric identification. The proposed system consists of three blocks: a feature extraction backbone based on short time Fourier transform (STFT), a contrastive learning network, and a classification network. We evaluated the proposed system on the Heartprint dataset, a new ECG biometrics multi-session dataset. The experimental analysis shows promising capabilities of the proposed method. In particular, it yields an average top1 accuracy of 98.02% on a new dataset built by gathering 1539 ECG records from 199 subjects collected in multiple sessions with an average interval between sessions of 47 days.

A Comprehensive Study of hassles in Aadhar Authentication and Biometric Security

A Comprehensive Study of hassles in Aadhar Authentication and Biometric Security

Recent Trends in the field of Biometric Security system using Digital Image Processing

Abstract: This paper gives an outline in security with the help of DIP(Digital Image Processing). Digital image processing technology is a heavily used technology in various fields like military, biometric, medical fields etc. Scientists try their best to upgrade this technology which helps in the future from any obstacles. In 1960 DIP was first invented at Bell Laboratory. In of case of Fingerprint, it is the evidence which plays the main role to solve criminal problems. Not only a fingerprint, face recognition and iris recognition also takes a main role to solve any criminal problems or it is also used for privacy purposes. Digital cameras take pictures with clarity and give a satisfied output to the users.

Artificial Multisensory Neuron with a Single Transistor for Multimodal Perception through Hybrid Visual and Thermal Sensing.

An artificial multisensory device applicable to in-sensor computing is demonstrated with a single-transistor neuron (1T-neuron) for multimodal perception. It simultaneously receives two sensing signals from visual and thermal stimuli. The 1T-neuron transforms these signals into electrical signals in the form of spiking and then fires them for a spiking neural network at the same time. This feature makes it feasible to realize input neurons for multimodal sensing. Visual and thermal sensing is achieved due to the inherent optical and thermal behaviors of the 1T-neuron. To demonstrate a neuromorphic multimodal sensing system with the artificial multisensory 1T-neuron, fingerprint recognition, widely used for biometric security, is implemented. Owing to the simultaneous sensing of heat as well as light, the proposed fingerprint recognition system composed of multisensory 1T-neurons not only identifies a genuine pattern but also judges whether or not it is forged.

Biometric authentication security enhancement under quantum dot light-emitting diode display via fingerprint imaging and temperature sensing

We improved biometric authentication security using dual recognition based on fingerprint image detection and skin-temperature-change sensing under quantum dot light-emitting diode (QLED) displays. QLEDs are more advantageous than organic light-emitting diodes (OLEDs) in terms of the contrast classification of patterns such as those in fingerprint recognition, owing to their narrow full-width-half-maximum. In this work, scattered, transmitted, and reflected light was captured from the top of the QLED, improving the digital luminance by 25%, as compared with that of OLEDs, because the electroluminescence spectra of the QLED were sustained, whereas those of the OLED were distorted by the generated noise peaks. A QLED with eight apertures sized up to tens of micrometers, mimicking the actual wiring structure of commercialized smartphones, was implemented to detect human fingerprints. The QLED using reduced graphene oxide as the temperature sensor detected temperature changes instantaneously upon finger touch, showing a 2% temperature response based on the human body temperature; however, the temperature change was less than 0.1% for spoof fingerprints printed on paper. Thus, this study successfully enhanced biometric authentication security, through fingerprint recognition based on image sensing using an optical system with micrometer-sized apertures and skin-temperature detection under QLED displays.

EEG-Based Person Identification and Authentication Using Deep Convolutional Neural Network

Using biometric modalities for person recognition is crucial to guard against impostor attacks. Commonly used biometric modalities, such as fingerprint scanners and facial recognition, are effective but can easily be tampered with and deceived. These drawbacks have recently motivated the use of electroencephalography (EEG) as a biometric modality for developing a recognition system with a high level of security. The majority of existing EEG-based recognition methods leverage EEG signals measured either from many channels or over a long temporal window. Both set limits on their usability as part of real-life security systems. Moreover, nearly all available methods use hand-engineered techniques and do not generalize well to unknown data. The few EEG-based recognition methods based on deep learning suffer from an overfitting problem, and a large number of model parameters must be learned from only a small amount of available EEG data. Leveraging recent developments in deep learning, this study addresses these issues and introduces a lightweight convolutional neural network (CNN) model consisting of a small number of learnable parameters that enable the training and evaluation of the CNN model on a small amount of available EEG data. We present a robust and efficient EEG-based recognition system using this CNN model. The system was validated on a public domain benchmark dataset and achieved a rank-1 identification result of 99% and an equal error rate of authentication performance of 0.187%. The system requires only two EEG channels and a signal measured over a short temporal window of 5 s. Consequently, this method can be used in real-life settings to identify or authenticate biometric security systems.

A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0

The Internet of Things (IoT) is being applied in multiple domains, including smart homes and energy management. This work aims to tighten security in IoTs using fingerprint authentications and avoid unauthorized access to systems for safeguarding user privacy. Captured fingerprints can jeopardize the security and privacy of personal information. To solve privacy- and security-related problems in IoT-based environments, Biometric Authentication Frameworks (BAFs) are proposed to enable authentications in IoTs coupled with fingerprint authentications on edge consumer devices and to ensure biometric security in transmissions and databases. The Honeywell Advanced Encryption Security-Cryptography Measure (HAES-CM) scheme combined with Hybrid Advanced Encryption Standards with Chaotic Map Encryptions is proposed. BAFs enable private and secure communications between Industry 4.0’s edge devices and IoT. This work’s suggested scheme’s evaluations with other encryption methods reveal that the suggested HAES-CM encryption strategy outperforms others in terms of processing speeds.

Face Templates Encryption Technique Based on Random Projection and Deep Learning

Cancellable biometrics is the solution for the trade-off between two concepts: Biometrics for Security and Security for Biometrics. The cancelable template is stored in the authentication system’s database rather than the original biometric data. In case of the database is compromised, it is easy for the template to be canceled and regenerated from the same biometric data. Recoverability of the cancelable template comes from the diversity of the cancelable transformation parameters (cancelable key). Therefore, the cancelable key must be secret to be used in the system authentication process as a second authentication factor in conjunction with the biometric data. The main contribution of this paper is to tackle the risks of stolen/lost/shared cancelable keys by using biometric trait (in different feature domains) as the only authentication factor, in addition to achieving good performance with high security. The standard Generative Adversarial Network (GAN) is proposed as an encryption tool that needs the cancelable key during the training phase, and the testing phase depends only on the biometric trait. Additionally, random projection transformation is employed to increase the proposed system’s security and performance. The proposed transformation system is tested using the standard ORL face database, and the experiments are done by applying different features domains. Moreover, a security analysis for the proposed transformation system is presented.

Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

Biometric security is a growing trend, as it supports the authentication of persons using confidential biometric data. Most of the transmitted data in multimedia systems are susceptible to attacks, which affect the security of these systems. Biometric systems provide sufficient protection and privacy for users. The recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of attacks. In addition, they have not been studied on different and large biometric datasets. Another point that deserves consideration is the hardware implementation of cancellable biometric recognition systems. This paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic cryptosystem. The rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates, and hence enhance the security and privacy of users. The suggested cryptosystem adds significant permutation and diffusion to the encrypted biometric templates. We introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against attacks. In addition, a Field Programmable Gate Array (FPGA) implementation of the proposed system is introduced. The obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes, such as Double Random Phase Encoding (DRPE) to reveal superiority, and hence high recognition performance of the proposed cancellable biometric recognition system. The obtained results prove that the proposed cryptosystem enhances the security and leads to better efficiency of the cancellable biometric recognition system in the presence of different types of attacks.