Biometric Authentication Techniques Research Articles

스마트폰 가속도 센서를 이용한 사용자 인증 방법 연구

With the growth of financial industry with smartphone, interest on user authentication using smartphone has been arisen in these days. There are various type of biometric user authentication techniques, but gait recognition using accelerometer sensor in smartphone does not seem to develop remarkably. This paper suggests the method of user authentication using accelerometer sensor embedded in smartphone. Specifically, calibrate the sensor data from smartphone with 3D-transformation, extract features from transformed data and do principle component analysis, and learn model with using gaussian mixture model. Next, authenticate user data with confidence interval of GMM model. As result, proposed method is capable of user authentication with accelerometer sensor on smartphone as a high degree of accuracy(about 96%) even in the situation that environment control and limitation are minimum on the research.

Keystroke Dynamics Authentication in Cloud Computing

Cloud computing needs a strong and efficient authentication system because the user will access his rented part through a faraway connection and it will make the authentication sensor device besides the user place for identification and verification so how to know the user who claimed himself to be the legal user. Keystroke identification system as a biometric authentication technique is strongly Candidate for the security issues in cloud computing technology. Keystroke dynamics as a security system did not need extra hardware because the authentication device will be the existing keyboard based on everyone has a unique style for writing. The other biometric methods are addressed with each advantage and disadvantage along with keystroke method. In this paper, all known studies about keystroke technique are explained and compared between them according to the classification technique, number of the participated users and each study results then introduces a survey on software and hardware of other biometric authentication techniques and after the literature review is addressed then keystroke as a biometric authentication system is suggested to access cloud computing environment because it has many advantages to being a part of the known security systems which spread in our world.

Periocular authentication based on FEM using Laplace–Beltrami eigenvalues

Periocular structure, a sub-region of face is characterized by spatial content with an ability to authenticate in non-intrusive civic E/M commerce surveillance applications. The current paper proposes a novel biometric authentication technique by utilizing geometric properties enduring a periocular region. Major portion of existing periocular pattern recognition methods is based on skin texture global information. The proposed approach quantifies periocular shape in ways that effectively find perceptually similar images in the database. The proposed work is an enhancement over state-of-the-art method which achieves low dimensional local periocular pattern data preserved on high dimensional non-linear plane. Periocular surface is discretized by triangular mesh cornered on natural edges. The Laplace–Beltrami eigen spectrum derived over the mesh using cotangent weighted Finite Element Method (FEM) implementation is implicitly compared to achieve periocular region shape based authentication.

Performance Evaluation of Different Feature Extractors and Classifiers for Recognition of Human Faces with Low Resolution Images

Face recognition is an effective biometric identification technique used in many applications such as law enforcement, document validation and video surveillance. In this paper the effect of low resolution images which are captured in real world applications, on the performance of different feature extraction techniques combined with a variety of classification approaches is evaluated. Gabor features and its combination with local phase quantization histogram (GLPQH) are dimensionality reduced by principal component analysis (PCA), linear discriminant analysis (LDA), locally sensitive discriminant analysis (LSDA) and neighbourhood preserving embedding (NPE) to extract discriminant image characteristics and the class label is attributed using the extreme learning machine (ELM), sparse classifier (SC), fuzzy nearest neighbour (FNN) or regularized discriminant classifier (RDC). ORL and AR databases are utilized and the results show that ELM and RDC have better performance and stability against resolution reduction, especially on Gabor-PCA and Gabor-LDA techniques. Among the interpolation approaches that we employed to enhance the image resolution, nearest neighbour outperforms other methods.

Fast fingerprint identification using GPUs

Fingerprints are widely used in a variety of biometric identification systems. The fingerprint matching process is a processing step whose computational requirements limit the size of the fingerprint database that can be dealt with.Fingerprint matching algorithms based on minutiae are one of the most relevant families of biometric identification techniques. The scalability of these models is determined not only by the number of fingerprints but also the number of minutiae per fingerprint. Therefore, processing millions of fingerprints per second requires being able to process hundreds of millions of minutiae per second.In this paper we present a new design of the minutiae based fingerprint matching algorithm presented by Jiang et al. specifically created for GPU based massively parallel architectures. The parallel design allows speed-up ratios of up to 15 with one GPU compared to multi-threaded CPU implementations, and up to 54 using several GPUs in parallel and fingerprint processing rates of between 300,000 and 1,500,000 fingerprints per second.

Surveying the Development of Biometric User Authentication on Mobile Phones

Designing reliable user authentication on mobile phones is becoming an increasingly important task to protect users' private information and data. Since biometric approaches can provide many advantages over the traditional authentication methods, they have become a significant topic for both academia and industry. The major goal of biometric user authentication is to authenticate legitimate users and identify impostors based on physiological and behavioral characteristics. In this paper, we survey the development of existing biometric authentication techniques on mobile phones, particularly on touch-enabled devices, with reference to 11 biometric approaches (five physiological and six behavioral). We present a taxonomy of existing efforts regarding biometric authentication on mobile phones and analyze their feasibility of deployment on touch-enabled mobile phones. In addition, we systematically characterize a generic biometric authentication system with eight potential attack points and survey practical attacks and potential countermeasures on mobile phones. Moreover, we propose a framework for establishing a reliable authentication mechanism through implementing a multimodal biometric user authentication in an appropriate way. Experimental results are presented to validate this framework using touch dynamics, and the results show that multimodal biometrics can be deployed on touch-enabled phones to significantly reduce the false rates of a single biometric system. Finally, we identify challenges and open problems in this area and suggest that touch dynamics will become a mainstream aspect in designing future user authentication on mobile phones.

Cubic Bezier Curve Approach for Automated Offline Signature Verification with Intrusion Identification

Authentication is a process of identifying person’s rights over a system. Many authentication types are used in various systems, wherein biometrics authentication systems are of a special concern. Signature verification is a basic biometric authentication technique used widely. The signature matching algorithm uses image correlation and graph matching technique which provides false rejection or acceptance. We proposed a model to compare knowledge from signature. Intrusion in the signature repository system results in copy of the signature that leads to false acceptance. Our approach uses a Bezier curve algorithm to identify the curve points and uses the behaviors of the signature for verification. An analyzing mobile agent is used to identify the input signature parameters and compare them with reference signature repository. It identifies duplication of signature over intrusion and rejects it. Experiments are conducted on a database with thousands of signature images from various sources and the results are favorable.

지능형 자동차 내부 네트워크에서 생체인증을 이용한 인증기법

현재 IT기술과 자동차기술을 융합한 지능형 자동차에 대한 연구가 활발히 진행중에 있으며 많은 새로운 서비스 모델들이 개발중에 있다. 지능형자동차에 대한 개발이 활발하게 이루어지면서 무선 네트워크를 이용한 다양한 서비스가 제공이 되고 있다. 이러한 지능형 자동차가 무선 네트워크를 이용한 서비스가 제동되면서 다양한 보안위협 요소가 도출되고 있다. 본 논문에서는 지능형 자동차의 외부에서 네트워크에 침입하여 지능형자동차의 보안위협 요소를 분석하고 지능형 자동차의 보안 솔루션의 인증 모델에 대한 기법을 제공하고자 한다. 인증 모델의 경우 신체의 고유성을 식별할 수 있는 생체인식을 이용한 인증기법을 제안한다. Studies on the intelligent vehicles that are fused with IT and intelligent vehicle technologies are currently under active discussion. And many new service models for them are being developed. As intelligent vehicles are being actively developed, a variety of wireless services are support. As such intelligent vehicles use wireless network, they are exposed to the diverse sources of security risk. This paper aims to examine the factors to threaten intelligent vehicle, which are usually intruded through network system and propose the security solution using biometric authentication technique. The proposed security system employs biometric authentication technique model that can distinguish the physical characteristics of user.

AUTHENTICATION OF FINGERPRINT BIOMETRICS ACQUIRED USING A CELLPHONE CAMERA: A REVIEW

Over the past decade, there have been dramatic increases in the usage of mobile phones in the world. Currently available smart mobile phones are capable of storing enormous amounts of personal information/data. The smart mobile phone is also capable of connecting to other devices, with the help of different applications. Consequently, with these connections comes the requirement of security to protect personal information. Nowadays, in many applications, a biometric fingerprint recognition system has been embedded as a primary security measure. To enable a biometric fingerprint recognition system in smart mobile phones, without any additional costs, a built-in high performance camera can be utilized. The camera can capture the fingerprint image and generate biometric traits that qualify the biometric fingerprint authentication approach. However, the images acquired by a mobile phone are entirely different from the images obtained by dedicated fingerprint sensors. In this paper, we present the current trend in biometric fingerprint authentication techniques using mobile phones and explore some of the future possibilities in this field.

An optical authentication system based on encryption technique and multimodal biometrics

Abstract A major concern nowadays for a biometric credential management system is its potential vulnerability to protect its information sources. To prevent a genuine user's templates from both internal and external threats, a novel and simple method combined optical encryption with multimodal biometric authentication technique is proposed. In this method, the standard biometric templates are generated real-timely by the verification keys owned by legal user so that they are unnecessary to be stored in a database. Compared with the traditional recognition algorithms, storage space and matching time are greatly saved. In addition, the verification keys are difficult to be forged due to the utilization of optical encryption technique. Although the verification keys are lost or stolen, they are useless for others in absence of the legal owner's biometric. A series of numerical simulations are performed to demonstrate the feasibility and performance of this method.

Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context

The information systems (IS) literature has long emphasized the importance of user acceptance of computer-based IS. Evaluating the determinants of acceptance of information technology (IT) is vital to address the problem of underutilization and leverage the benefits of IT investments, especially for more radical technologies. This study examines individual acceptance of biometric identification techniques in a voluntary environment, measuring the intention to accept and further recommend the technology resulting from a carefully selected set of variables. Drawing on elements of technology acceptance model (TAM), diffusion of innovations (DOI) and unified theory of acceptance and use of technology (UTAUT) along with the trust-privacy research field, we propose an integrated approach that is both theoretically and empirically grounded. By testing some of the most relevant and well-tested elements from previous models along with new antecedents to biometric system adoption, this study produces results which are both sturdy and innovative. We first confirm the influence of renowned technology acceptance variables such as compatibility, perceived usefulness, facilitating conditions on biometrics systems acceptance and further recommendation. Second, prior factors such as concern for privacy, trust in the technology, and innovativeness also prove to have an influence. Third, unless innovativeness, the most important drivers to explain biometrics acceptance and recommendation are not from the traditional adoption models (TAM, DOI, and UTAUT) but from the trust and privacy literature (trust in technology and perceived risk).

ID Proof on the Go: Development of a Mobile EEG-Based Biometric Authentication System

In recent years, the need for greater security for storing personal and business data or accessing corporate networks on mobile devices has been growing rapidly, and one of the potential solutions is to employ innovative biometric authentication techniques. This article presents the development of a mobile biometric authentication system based on electroencephalogram (EEG) recordings in combination with already proven technologies such as facial detection and nearfield communication (NFC). The overall goal of this work is to fill the gap between mobile Web technologies and wireless EEG devices and to develop a new authentication technique and a feasible application. Therefore, we review the relevant literature, conduct several EEG measurement experiments, and discuss the procedure and results with experts in the EEG and digital signal processing (DSP) fields. On the basis of these results, we build and present a mobile prototype system capable of authenticating users based on the uniqueness of their brain waves. Furthermore, we implement a novel authentication process that will make the authentication system more secure. We also give suggestions for future improvements to the system.

Secure Method for Biometric-Based Recognition with Integrated Cryptographic Functions

Biometric systems refer to biometric technologies which can be used to achieve authentication. Unlike cryptography-based technologies, the ratio for certification in biometric systems needs not to achieve 100% accuracy. However, biometric data can only be directly compared through proximal access to the scanning device and cannot be combined with cryptographic techniques. Moreover, repeated use, improper storage, or transmission leaks may compromise security. Prior studies have attempted to combine cryptography and biometrics, but these methods require the synchronization of internal systems and are vulnerable to power analysis attacks, fault-based cryptanalysis, and replay attacks. This paper presents a new secure cryptographic authentication method using biometric features. The proposed system combines the advantages of biometric identification and cryptographic techniques. By adding a subsystem to existing biometric recognition systems, we can simultaneously achieve the security of cryptographic technology and the error tolerance of biometric recognition. This method can be used for biometric data encryption, signatures, and other types of cryptographic computation. The method offers a high degree of security with protection against power analysis attacks, fault-based cryptanalysis, and replay attacks. Moreover, it can be used to improve the confidentiality of biological data storage and biodata identification processes. Remote biometric authentication can also be safely applied.

Biometric citizenship

The article considers the issue of citizenship in light of the recent developments in biometric identification techniques. It aims to answer the question as to what kind of citizenship is the ‘biometric citizenship’. Drawing on several empirical examples including the Iris Recognition Immigration System scheme, identity cards and current citizenship reform plans in the UK, I argue that biometric citizenship is at once a ‘neoliberal citizenship’ and a ‘biological citizenship’. The neoliberal aspect of biometric citizenship is demonstrated through the rearrangement of the experience of border crossing in terms of the neoliberal ethos of choice, freedom, active entrepreneurialism and transnational expedited mobility. At the same time, these are enacted alongside the exclusionary and violent measures directed at those who are considered as risky categories illustrating the constitutive relationship between the ‘biometric citizen’ and its ‘other’. As regards its biological aspect, biometric citizenship is embedded within rationalities and practices that deploy the body not only as a means of identification but also as a way of sorting through different forms of life according to their degree of utility and legitimacy in relation to market economy. This aspect also carries a racial and national dimension exemplified in both the national identity card scheme and the very technical infrastructure of biometric technology. Overall, what these two features have in common is the reduction of the principle of citizenship to processes of identity management and technical procedures without, however, purging it altogether from its all too familiar national and race-based components.

New Minutiae-Matching Method Based on Partial Fingerprints

As information technologies have advanced greatly in the recent years, the security problem of information networks becomes all the more important. As a result, biometric identification techniques have been given considerable attention. Fingerprint-related techniques, due to their desirable properties, e.g., universality, perpetuity, collectability, and particularity, are most widely applied and documented. However, in practice, collected fingerprint images are not always of good shape. They often are noisy or contain only partial fingerprints. Therefore, in this article, the authors propose a partial fingerprint comparison technique, called the triangular scheme, to combine with a coefficient of orientation deflection variation and other advancements, to obtain satisfactory matching based on only partial fingerprints. By the proposed scheme, they show that within a wide range of average reliability when the test image and a database image could have only five matched minutia points, the authors have both fault resistance rate and fault acceptance rate error values down to 29%, and the total matching accuracy reaches 71%.

Multifinger Feature Level Fusion Based Fingerprint Identification

(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 11, 2012

An Enhanced Authenticated Key Agreement Protocol with a Neural Network-based Model for Joining-Phase in Mobile Environments

Providing secure communication for mobile environment is very important. In such environments, it is envisaged that users (through one of their mobile devices and via some network access technologies) should be able to seamlessly discover, select, and access a rich offering of services from a range of service providers. Password-based and ID-based authentication schemes are widely used but they still have potential threats that the passwords and verification table may be read, or altered by intruder. Moreover, the user’s privacy is not achieved especially if a principal’s privacy is to be protected. On the other side, Neural Network has been attracting more and more researchers since the past decades. The properties such as parameter sensitivity, random similarity, learning ability, etc., make it very suitable for information protection and privacy preserving. In this paper we present a Neural based secure joining phase with a biometric based Authenticated Key Agreement (NB-EC-SAKA). Our proposed protocol provides privacy, biometric-based secure mutual authentication, key establishment and key confirmation over an untrusted network. Our protocol is a based on EC-SAKA protocol [16], on Elliptic Curve Cryptography, and on EC-digital signature. The new proposal relies on [16] to achieve many of the required security and performance properties including dictionary attacks, Man- In-The middle attack, perfect forward secrecy which protects past sessions and passwords against future compromise, etc. This paper presents a review on the biometric authentication and neural networks techniques and presents a new scheme where user’s security and privacy are preserved. The purpose of our scheme is to ensure that the rendered services are accessed only by a legitimate user and not anyone else and while privacy is preserved.

Iris Biometric Recognition for Person Identification in Security Systems

The security is an important aspect in our daily life whichever the system we consider security plays vital role. The biometric person identification technique based on the pattern of the human iris is well suited to be applied to access control and provides strong e-security. Security systems having realized the value of biometrics for two basic purposes: to verify or identify users. In this paper we focus on an efficient methodology for identification and verification for iris detection, even when the images have obstructions, visual noise and different levels of illuminations and we use the CASIA iris database it will also work for UBIRIS Iris database which has images captured from distance while moving a person. Efficiency is acquired from iris detection and recognition when its performance evaluation is accurate.

Performance issues in biometric authentication based on information theoretic concepts: A review

Many of the performance evaluation techniques for biometric authentication use error probabilities to yield a measure called receiver operating characteristic (ROC). The ROC is based on the Neyman-Pearson hypothesis testing and is obtained by varying a threshold for decision making. This measure is dependent on database partitioning and choice of thresholds. Also, obtaining the probability distributions, and thus the ROC, is computationally complex. Recent approaches based on information theoretic models partially overcome these limitations and also provide insight into the performance of biometric authentication techniques. Measures in line with obtaining Chernoff capacity and with Shannon capacity have been proposed, and are respectively called recognition capacity and constrained capacity. Measures which are largely independent of data size and quality are based on the minimization of false matches between templates and are good indicators of biometrics uniqueness or, equivalently, its random correspondence. The parameters related to confidence intervals are however obtained empirically. One such measure is obtained from the probability distribution of the Hamming distance between templates of the iris biometrics. Also, relative entropy of features between a user and the population yields a measure of uniqueness for face biometrics. Another approach is to measure the probability of this random correspondence (PRC). PRC of a fingerprint biometrics has been obtained using compound statistical distributions. Another formulation of PRC is based on a rate-distortion framework by applying a distortion constraint to a codebook of binarized features. Biometric templates are akin to noisy source symbols in an information theoretic setup. A bound on the PRC has been obtained by developing error exponents of noisy biometrics represented in terms of a binary source-channel model. The method has low computational complexity, is not limited to a specific biometrics, and does not require empirically obtained confidence intervals.

A non-intrusive biometric authentication mechanism utilising physiological characteristics of the human head

This paper proposes and evaluates a non-intrusive biometric authentication technique drawn from the discrete areas of biometrics and Auditory Evoked Responses. The technique forms a hybrid multi-modal biometric in which variations in the human voice due to the propagation effects of acoustic waves within the human head are used to verify the identity of a user. The resulting approach is known as the Head Authentication Technique (HAT). Evaluation of the HAT authentication process is realised in two stages. First, the generic authentication procedures of registration and verification are automated within a prototype implementation. Second, a HAT demonstrator is used to evaluate the authentication process through a series of experimental trials involving a representative user community. The results from the trials confirm that multiple HAT samples from the same user exhibit a high degree of correlation, yet samples between users exhibit a high degree of discrepancy. Statistical analysis of the prototype performance realised system error rates of 6% False Non-Match Rate (FNMR) and 0.025% False Match Rate (FMR).