Multi classifier-based score level fusion of multi-modal biometric recognition and its application to remote biometrics authentication

Biometric recognition has become a common and reliable way to authenticate the identity of a person. Multimodal biometrics has become an interest of areas for researches in the recent past as it provides more reliability and accuracy. In multimodal biometric recognition, score level fusion has been a very promising approach to improve the overall system's accuracy. In this paper, score level fusion is carried out using three categories of classifiers like, rule classifier (fuzzy classifier), lazy classifier (Naïve Bayes) and learning classifiers (ABC-NN). These three classifiers have their own advantages and disadvantages so the hybridization of classifiers leads to provide overall improvements. The proposed technique consists of three modules, namely processing module, classifier module and combination module. Finally, the proposed fusion method is applied to remote biometric authentication. The implementation is carried out using MATLAB and the evaluation metrics employed are False Acceptance Rate (FAR), False Rejection Rate (FRR) and accuracy. The proposed technique is also compared with other techniques and by employing various combinations of modalities. From the results, we can observe that the proposed technique has achieved better accuracy value and Receiver Operating Characteristic (ROC) curves when compared to other techniques. The proposed technique reached maximum accuracy of having 95% and shows the effectiveness of the proposed technique.     

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.     

Quantum Homomorphic Signature with Repeatable Verification

In January 2015, the first quantum homomorphic signature scheme was proposed creatively. However, only one verifier is allowed to verify a signature once in this scheme. In order to support repeatable verification for general scenario, we propose a new quantum homomorphic signature scheme with repeatable verification by introducing serial verification model and parallel verification model. Serial verification model solves the problem of signature verification by combining key distribution and Bell measurement. Parallel verification model solves the problem of signature duplication by logically treating one particle of an EPR pair as a quantum signature and physically preparing a new EPR pair. These models will be beneficial to the signature verification of general scenarios. Scheme analysis shows that both intermediate verifiers and terminal verifiers can successfully verify signatures in the same operation with fewer resource consumption, and especially the verified signature in entangled states can be used repeatedly.     

Code-based Sequential Aggregate Signature Scheme

This paper proposes the first code-based quantum immune sequential aggregate signature (SAS) scheme and proves the security of the proposed scheme in the random oracle model. Aggregate signature (AS) schemes and sequential aggregate signature schemes allow a group of potential signers to sign different messages respectively, and all the signatures of those users on those messages can be aggregated into a single signature such that the size of the aggregate signature is much smaller than the total size of all individual signatures. Because of the aggregation of many signatures into a single short signature, AS and SAS schemes can reduce bandwidth and save storage; moreover, when a SAS is verified, not only the valid but also the order in which each signer signed can be verified. AS and SAS schemes can be applied to traffic control, banking transaction and military applications. Most of the existing AS and SAS schemes are based either on pairing or Rivest–Shamir–Adleman (RSA), and hence, can be broken by Shor’s quantum algorithm for Integer Factoring Problem (IFP) and Discrete Logarithm Problem (DLP). There are no quantum algorithms to solve syndrome decoding problems. Hence, code-based cryptography is seen as one of the promising candidates for post-quantum cryptography. This paper shows how to construct quantum immune sequential aggregate signatures based on coding theory. Specifically, we construct our scheme with the first code based signature scheme proposed by Courtois, Finiasz and Sendrier (CFS). Compared to the CFS signature scheme without aggregation, the proposed sequential aggregate signature scheme can save about 90% storage when the number of signers is asymptotically large.     

Providing multimodal biometric authentication using five competent traits

Abstract

Recognition accuracy of a single biometric authentication system is often much limited; hence, a multimodal biometric approach for identity verification is proposed. A new way of person authentication based on five-competent traits, namely, iris, ear, palm print, fingerprint and retina, is proposed in this paper. Each metric is analysed individually to get the matching scores from the corresponding feature sets. These scores are then combined using weighted sum fusion rule. In order to provide liveness verification for our authentication system, we employ the retinal blood vessel pattern recognition. To validate our approach, several experiments were conducted on the images obtained from five different datasets. The experimental results reveal that this multimodal biometric verification system is much more reliable and precise than the single biometric approaches.     

ExpressionHash: Securing Telecare Medical Information Systems Using BioHashing

The COVID-19 outbreak and its medical distancing phenomenon have effectively turned the global healthcare challenge into an opportunity for Telecare Medical Information Systems. Such systems employ the latest mobile and digital technologies and provide several advantages like minimal physical contact between patient and healthcare provider, easy mobility, easy access, consistent patient engagement, and cost-effectiveness. Any leakage or unauthorized access to users’ medical data can have serious consequences for any medical information system. The majority of such systems thus rely on biometrics for authenticated access but biometric systems are also prone to a variety of attacks like spoofing, replay, Masquerade, and stealing of stored templates. In this article, we propose a new cancelable biometric approach which has tentatively been named as “Expression Hash” for Telecare Medical Information Systems. The idea is to hash the expression templates with a set of pseudo-random keys which would provide a unique code (expression hash). This code can then be serving as a template for verification. Different expressions would result in different sets of expression hash codes, which could be used in different applications and for different roles of each individual. The templates are stored on the server-side and the processing is also performed on the server-side. The proposed technique is a multi-factor authentication system and provides advantages like enhanced privacy and security without the need for multiple biometric devices. In the case of compromise, the existing code can be revoked and can be directly replaced by a new set of expression hash code. The well-known JAFFE (The Japanese Female Facial Expression) dataset has been for empirical testing and the results advocate for the efficacy of the proposed approach.     

Achieving Privacy-Preserving Iris Identification Via El Gamal

Currently, many biometric systems maintain the user’s biometrics and templates in plaintext format, which brings great privacy risk to uses’ biometric information. Biometrics are unique and almost unchangeable, which means it is a great concern for users on whether their biometric information would be leaked. To address this issue, this paper proposes a confidential comparison algorithm for iris feature vectors with masks, and develops a privacy-preserving iris verification scheme based on the El Gamal encryption scheme. In our scheme, the multiplicative homomorphism of encrypted features is used to compare of iris features and their mask information. Also, this paper improves the Hamming distance of iris features, which makes the similarity matching work better than existing ones. Experimental results confirm the practicality of our proposed schemes in real world applications, that is, for the iris feature vectors and masks of 2048 bits, nearly 12 comparisons can be performed per second.     

一种基于自验证公钥的门限代理签名方案的安全性分析

Xue和Cao提出了一种基于自验证公钥的门限代理签名方案,然而,该方案是不安全的。给出了对该方案一种攻击：攻击者获得一个合法的原始签名人发送给代理签名人的签名了的授权证书以及代理签名人生成的一个有效的代理签名后,能够伪造出一个新的对相同消息的代理签名,而原始签名人变为攻击者自己,由于验证者并不能验证代理签名人到底是代表谁生成了代理签名,这样,攻击者就获得了与合法原始签名人相同的权益。特别地,代理签名人代表原始签名人生成的门限代理签名可以被转化为普通的门限签名。分析了该方案存在安全漏洞的原因并提出了改进措施,改进措施能有效地弥补原方案存在的安全缺陷。     

An Advanced Quantum-Resistant Signature Scheme for Cloud Based on Eisenstein Ring

Signature, widely used in cloud environment, describes the work as readily identifying its creator. The existing signature schemes in the literature mostly rely on the Hardness assumption which can be easily solved by quantum algorithm. In this paper, we proposed an advanced quantum-resistant signature scheme for Cloud based on Eisenstein Ring (ETRUS) which ensures our signature scheme proceed in a lattice with higher density. We proved that ETRUS highly improve the performance of traditional lattice signature schemes. Moreover, the Norm of polynomials decreases significantly in ETRUS which can effectively reduce the amount of polynomials convolution calculation. Furthermore, storage complexity of ETRUS is smaller than classical ones. Finally, according to all convolution of ETRUS enjoy lower degree polynomials, our scheme appropriately accelerate 56.37% speed without reducing its security level.     

Dual watermarking technique with multiple biometric watermarks

In order to improve the robustness of the watermarking algorithm, a dual watermarking method is proposed to prove copyright ownership. Visible watermarking is important for protecting online resources from unauthorized reproduction. However robust, visible watermarks are vulnerable to illegal removal and other common signal processing and geometric attacks. Multiple invisible watermarks can enhance the protection of the visibly watermarked image. When the ownership of tampered image is in question, the invisible watermark can be extracted to provide appropriate ownership information. We have proposed dual watermarking scheme with multiple biometric watermarks in which it embeds speech and face biometric traits of owner invisibly and lastly offline signature is overlaid translucently on image. Before embedding, speech is compressed using Linear Predictive Coding (LPC) and Gabor face is created from face biometric trait. All three watermarks Gabor face, LPC coefficients and offline signature are the biometric characteristics of the owner and hence they are highly related with copyright holder. The proposed scheme is robust enough, Gabor face and LPC coefficients can be extracted from the signature marked image or even from the tampered image from which signature is removed illegally or legally. As multiple watermarks are embedded at least one watermark survives under different attacks. It can find application for joint ownership or to address single ownership multiple times.     

Content-based searching of multimedia databases by use of approximate digital signatures

We propose the use of approximate digital signatures of selected multimedia feature vectors for fast content-based retrieval in large multimedia databases. We adapt and extend the approximate message authentication code (AMAC), introduced by some of the authors recently in the area of message authentication, to the multimedia searching problem. An AMAC is a binary signature with the ability to reflect changes in the message it represents. The Hamming distance between two AMACs is used to measure the degree of the similarity between multimedia objects. We develop a method to compress AMAC signatures to create a direct look-up table that allows for fast searching of a database. The color histogram is used as the example feature space to show how the signature is applied. Experimental results show that the performance of the proposed method is comparable with existing methods based on other popular metrics, but it significantly decreases search time.     

Automated Patient Discomfort Detection Using Deep Learning

The Internet of Things (IoT) has been transformed almost all fields of life, but its impact on the healthcare sector has been notable. Various IoT-based sensors are used in the healthcare sector and offer quality and safe care to patients. This work presents a deep learning-based automated patient discomfort detection system in which patients’ discomfort is non-invasively detected. To do this, the overhead view patients’ data set has been recorded. For testing and evaluation purposes, we investigate the power of deep learning by choosing a Convolution Neural Network (CNN) based model. The model uses confidence maps and detects 18 different key points at various locations of the body of the patient. Applying association rules and part affinity fields, the detected key points are later converted into six main body organs. Furthermore, the distance of subsequent key points is measured using coordinates information. Finally, distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions. The accuracy of the proposed system is assessed on various test sequences. The experimental outcomes reveal the worth of the proposed system’ by obtaining a True Positive Rate of 98% with a 2% False Positive Rate.     

Biometric verification with correlation filters

Using biometrics for subject verification can significantly improve security over that of approaches based on passwords and personal identification numbers, both of which people tend to lose or forget. In biometric verification the system tries to match an input biometric (such as a fingerprint, face image, or iris image) to a stored biometric template. Thus correlation filter techniques are attractive candidates for the matching precision needed in biometric verification. In particular, advanced correlation filters, such as synthetic discriminant function filters, can offer very good matching performance in the presence of variability in these biometric images (e.g., facial expressions, illumination changes, etc.). We investigate the performance of advanced correlation filters for face, fingerprint, and iris biometric verification.     

Chinese signature verification: The topological approach and the waveform matching approach

Two approaches are proposed for Chinese signature verification in this article. The topological approach, which extracts the static topological features of the Chinese signature characters such as blocks, holes, segments, end-points, and cross-points, is first presented. Based on this approach, an automatic Chinese signature verification system is designed and implemented. Experimental results demonstrate a recognition rate of 94%. Two new algorithms for this approach are proposed in this article. The waveform matching approach to measure the similarity of waveforms is then presented. This approach cuts the waveforms into a string of valley and peak (V-P-V) units and calculates the distance between two strings by using insertion, deletion, and substitution to obtain the similarity of two waveforms. The time complexity of this algorithm is O(n × m), where n and m are the unit numbers of the two waveform strings, respectively. The V-P-V algorithm used in a Chinese signature verification system achieves a recognition rate of 90%. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 355–362, 1999     

Hidden identity-based signatures

This study introduces hidden identity-based signatures (Hidden-IBS), a type of digital signatures that provide mediated signer-anonymity on top of Shamir's identity-based signatures. The motivation of the new signature primitive is to resolve an important issue with the kind of anonymity offered by 'group signatures' where it is required that either the group membership list be public for opening signatures or that the opening authority be dependent on the group manager for its operation. Contrary to this, Hidden-IBS does not require the maintenance of a group membership list for opening signatures and they enable an opening authority that is totally independent of the group manager. As the authors argue this makes Hidden-IBS much more attractive than group signatures for a number of applications. In this study, the authors provide a formal model of Hidden-IBS as well as two efficient constructions that realise the new primitive. To demonstrate the power of the new primitive, the authors apply it to solve a problem of current onion-routing systems focusing on the Tor system in particular.     

Stealth technology for wind turbines

Currently, a large proportion of proposed UK wind farms have either concerns raised at the pre-planning stage or formal objections made by radar operators on the basis of the potential for wind turbines to cause interference to radar systems. The current generation of on and off-shore three-bladed horizontal axis wind turbines have radar signatures consistent with their often very large physical size and hence considerable potential to reduce the ability of ground-based radars to detect targets in the vicinity of the farm. The impact of wind farms, particularly on ground-based aviation radars such as those operated for air defence and military and civil air traffic control purposes is likely to become particularly acute as European Union member governments strive to meet the requirements for energy generation under the Renewables Obligation. In addition, the increasing number of offshore wind farm projects proposed has the potential to cause interference to marine radars such as coastal vessel traffic services and those on-board vessels for navigational purposes. This study considers the options available for the reduction of turbine radar signature and presents solutions for each of the main external turbine components. The radar signature reduction approaches are based on existing technologies developed for aerospace stealth applications. However, the realisation of these for the purposes of reducing wind turbine radar signatures is a novel development, particularly in the solutions proposed. The reduction of wind turbine-induced radar interference is a growing area of research.     

传感层反馈型机内测试系统模型分析和验证

虚警率较高是制约机内测试(Built-in Test,BIT)技术发展与应用的关键问题之一.为降低虚警率,从BIT系统的信息处理流程和虚警率的数学模型两方面分析了传感层不确定性对虚警率的影响,阐述了基于传感器系统可靠性模型的传感层动态不确定性分析和计算方法;在BIT系统中嵌入传感数据证实模块,提出了传感层反馈型BIT系统模型;对新模型和原模型的输出数据不确定性及虚警率进行了分析和验证.结果表明:传感层反馈型BIT系统模型可以有效降低传感层输出数据的不确定性和虚警率.     

基于小波分解的 K-分布 SAR 图像舰船检测

舰船目标检测是合成孔径雷达海洋应用的一个重要组成部分. 通过对合成孔径雷达图像中舰船目标和海杂波背景的结构差异特点进行分析,提出了一种利用小波分解技术和 K-分布海杂波模型的恒虚警率舰船目标检测方法,并对实际 SIR-C C 波段 SAR 图像进行了实验. 实验结果表明,该方法能够在复杂相干斑和海杂波背景中大幅增强舰船目标,并且有效保证了检测结果的准确性.     

Smartphone User Authentication Based on Holding Position and Touch-Typing Biometrics

In this advanced age, when smart phones are the norm, people utilize social networking, online shopping, and even private information storage through smart phones. As a result, identity authentication has become the most critical security activity in this period of the intelligent craze. By analyzing the shortcomings of the existing authentication methods, this paper proposes an identity authentication method based on the behavior of smartphone users. Firstly, the sensor data and touch-screen data of the smart phone users are collected through android programming. Secondly, the eigenvalues of this data are extracted and sent to the server. Thirdly, the Support Vector Machine (SVM) and Recurrent Neural Network (RNN) are introduced to train the collected data on the server end, and the results are finally yielded by the weighted average. The results show that the method this paper proposes has great FRR (False Reject Rate) and FAR (False Accept Rate).