Biometrics: a tool for information security

Establishing identity is becoming critical in our vastly interconnected society. Questions such as "Is she really who she claims to be?," "Is this person authorized to use this facility?," or "Is he in the watchlist posted by the government?" are routinely being posed in a variety of scenarios ranging from issuing a driver's license to gaining entry into a country. The need for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. Biometrics, described as the science of recognizing an individual based on his or her physical or behavioral traits, is beginning to gain acceptance as a legitimate method for determining an individual's identity. Biometric systems have now been deployed in various commercial, civilian, and forensic applications as a means of establishing identity. In this paper, we provide an overview of biometrics and discuss some of the salient research issues that need to be addressed for making biometric technology an effective tool for providing information security. The primary contribution of this overview includes: 1) examining applications where biometric scan solve issues pertaining to information security; 2) enumerating the fundamental challenges encountered by biometric systems in real-world applications; and 3) discussing solutions to address the problems of scalability and security in large-scale authentication systems.     

PPG-based human identification using Mel-frequency cepstral coefficients and neural networks

Multimedia Tools and Applications - One of the known problems in security systems is to identify persons based on certain signatures. Biometrics have been adopted in security systems to identify...     

Encoding local image patterns using Riesz transforms: With applications to palmprint and finger-knuckle-print recognition

Biometrics authentication is an effective method for automatically recognizing a person's identity with high confidence. It is well recognized that in biometric systems feature extraction and representation are key considerations. Among various feature extraction and representation schemes, coding-based methods are most attractive because they have the merits of high accuracy, robustness, compactness and high matching speed, and thus they have been adopted in many different kinds of biometric systems, such as iris, palmprint, and finger-knuckle-print based ones. However, how to devise a good coding scheme is still an open issue. Recent studies in image processing and applied mathematics have shown that local image features can be well extracted with Riesz transforms in a unified framework. Thus, in this paper we propose to utilize Riesz transforms to encode the local patterns of biometric images. Specifically, two Riesz transforms based coding schemes, namely RCode1 and RCode2, are proposed. They both use 3-bits to represent each code and employ the normalized Hamming distance for matching. RCode1 and RCode2 are thoroughly evaluated and compared with the other 3-bit coding methods on a palmprint database and a finger-knuckle-print database. Experiments show that the proposed methods, especially RCode2, could achieve quite similar verification accuracies with the state-of-the-art method (CompCode) while they need much less time at the feature extraction stage, which renders them better candidates for time critical applications.     

Biometric authentication

In this age of digital impersonation, biometric techniques are being used increasingly as a hedge against identity theft. The premise is that a biometric - a measurable physical characteristic or behavioral trait - is a more reliable indicator of identity than legacy systems such as passwords and PINs. There are three general ways to identify yourself to a computer system, based on what you know, what you have, or who you are. Biometrics belong to the "who you are" class and can be subdivided into behavioral and physiological approaches. Behavioral approaches include signature recognition, voice recognition, keystroke dynamics, and gait analysis. Physiological approaches include fingerprints; iris and retina scans; hand, finger, face, and ear geometry; hand vein and nail bed recognition; DNA; and palm prints. In this article, we focus on the two most popular biometric techniques: fingerprints and iris scans.     

Physimetric identification (Physi-ID)—Applying biometric concept in physical object identification

By relying on business intelligence technologies, services can be delivered to customers automatically by computer systems. To provide the right services to the right person, a methodology that precisely identifies a personal's identity must be in place. Biometrics offers a secure and reliable method for computerized personal identification and authentication. It accurately recognizes and determines the unique identity of a person based on her physiological and/or behavioral characteristics. In the case of physical objects, they may also be required to be identified automatically in order to provide additional services, such as at checkout counters of supermarkets or customs clearance checkpoints. Numerous crimes and business losses (e.g. counterfeit products) are related to mis-identification of physical objects. This paper introduces physimetric identification, an approach that applies the concept of biometrics for physical object identification. It addresses the problem through authenticating physical objects based on their unique physical and/or chemical characteristics. Apart from introducing the concept of physimetric identification, issues such as real applications, deployment considerations and limitations of the proposed technology will also be discussed.     

Fingerprint-Based Identity Authentication and Digital Media Protection in Network Environment

Current information security techniques based on cryptography are facing a challenge of lacking the exact connection between cryptographic key and legitimate users. Biometrics, which refers to distinctive physiological and behavioral characteristics of human beings, is a more reliable indicator of identity than traditional authentication system such as passwords-based or tokens-based. However, researches on the seamless integration biometric technologies, e.g., fingerprint recognition, with cryptosystem have not been conducted until recent years. In this paper, we provide an overview of recent advancements in fingerprint recognition algorithm with a special focus on the enhancement of low-quality fingerprints and the matching of the distorted fingerprint images, and discuss two representative methods of key release and key generation scheme based on fingerprints. We also propose two solutions for the application in identity authentication without trustworthy third-party in the network environment, and application in digital media protection, aiming to assure the secrecy of fingerprint template and fingerprint-based user authentication.     

Fingerprint classification: a review

Biometrics is the automatic identification of an individual that is based on physiological or behavioural characteristics. Due to its security-related applications and the current world political climate, biometrics is currently the subject of intense research by both private and academic institutions. Fingerprints are emerging as the most common and trusted biometric for personal identification. The main objective of this paper is to review the extensive research that has been done on fingerprint classification over the last four decades. In particular, it discusses the fingerprint features that are useful for distinguishing fingerprint classes and reviews the methods of classification that have been applied to the problem. Finally, it presents empirical results from the state of the art fingerprint classification systems that have been tested using the NIST Special Database 4.     

Graphics and security: exploring visual biometrics

Biometrics technology has come a long way from simpler forms of systems security. But are biometrics-based systems more secure or do they simply require crackers to become more proficient at breaking into systems? To recognize your fingerprint requires that a template of your fingerprint actually be present in the system that verifies your access. If you want to pass as somebody else, presumably you'd have to either have that person's finger with you or you'd need to change the verifying template residing in the system that verifies your print. Cracking into a system and replacing a legitimate print with your own isn't easy to do unless the system's security is poor. While biometric proponents stress the strength of their proprietary technologies or biometrics in general, no system is ever completely secure. Contrary to what many biometric proponents would have us believe-that biometric security outclasses traditional forms of security-all biometric systems are, after all, another form of computer security with its own set of strengths and weaknesses. Biometrics effectively trade some amount of privacy and cost effectiveness for ultimate convenience-and these systems are certainly no less secure than standard password systems. Password systems are cheap. Complex biometric scanning equipment is usually expensive. But biometrics seems to be where the industry is headed.     

A fast feature selection technique in multi modal biometrics using cloud framework

Biometrics refers to the process that uses biological or physiological traits to identify individuals. The progress seen in technology and security has a vital role to play in Biometric recognition which is a reliable technique to validate individuals and their identity. The biometric identification is generally based on either their physical traits or their behavioural traits. The multimodal biometrics makes use of either two or more of the modalities to improve recognition. There are some popular modalities of biometrics that are palm print, finger vein, iris, face or fingerprint recognition. Another important challenge found with multimodal biometric features is the fusion, which could result in a large set of feature vectors. Most biometric systems currently use a single model for user authentication. In this existing work, a modified method of heuristics that is efficiently used to identify an optimal feature set that is based on a wrapper-based feature selection technique. The proposed method of feature selection uses the Ant Colony Optimization (ACO) and the Particle Swarm Optimization (PSO) are used to feature extraction and classification process utilizes the integration of face, and finger print texture patterns. The set of training images is converted to grayscale. The crossover operator is applied to generate multiple samples for each number of images. The wok proposed here is pre-planned for each weight of each biometric modality, which ensures that even if a biometric modality does not exist at the time of verification, a person can be certified to provide calculated weights the threshold value. The proposed method is demonstrated better result for fast feature selection in bio metric image authentication and also gives high effectiveness security.     

Homogeneity of relative risk against two-step alternatives

In this paper the computational aspects of testing the null hypothesis of homogeneity of relative risk against two-step alternatives are examined. This representation is the same as that introduced by Anderson and Senthilselvan (Appl. Stat. 31 (1982) 44-51), i.e. a two-step model. Such alternatives may be used to represent decay in effect or, perhaps, inversion of the regression effect or crossing hazards. For such models inferential aspects are slightly more involved than for instance with proportional hazards models having fixed effects, even when time dependent as in O'Quigley and Pessione (Biometrics 45 (1989) 135-144). The necessary techniques for carrying out tests based on the two-stage model have recently been developed (O'Quigley and Pessione (Biometrics (1990) (in press] and in this paper we outline the necessary steps to be taken in the construction of algorithms to implement the proposed procedures. Programs enabling analyses based on the assumption of homogeneity of risk are very widely available. These include software packages such as BMDP, SAS, SPSS and GLIM. In the output of these packages, as well as that from most other standard routines, is contained all the necessary information to carry out the tests proposed by O'Quigley and Pessione. Here we detail the explicit formulae needed for carrying out the calculations in practice. The special cases of crossing hazards are considered in detail.     

Ensemble of local and global information for finger-knuckle-print recognition

Biometrics authentication is an effective method for automatically recognizing a person’s identity. Recently, it has been found that the finger-knuckle-print (FKP), which refers to the inherent skin patterns of the outer surface around the phalangeal joint of one’s finger, has high capability to discriminate different individuals, making it an emerging biometric identifier. In this paper, based on the results of psychophysics and neurophysiology studies that both local and global information is crucial for the image perception, we present an effective FKP recognition scheme by extracting and assembling local and global features of FKP images. Specifically, the orientation information extracted by the Gabor filters is coded as the local feature. By increasing the scale of Gabor filters to infinite, actually we can get the Fourier transform of the image, and hence the Fourier transform coefficients of the image can be taken as the global features. Such kinds of local and global features are naturally linked via the framework of time-frequency analysis. The proposed scheme exploits both local and global information for the FKP verification, where global information is also utilized to refine the alignment of FKP images in matching. The final matching distance of two FKPs is a weighted average of local and global matching distances. The experimental results conducted on our FKP database demonstrate that the proposed local-global information combination scheme could significantly improve the recognition accuracy obtained by either local or global information and lead to promising performance of an FKP-based personal authentication system.     

生物特征识别中的信息融合技术

网络信息化时代的一大特征就是身份的数字化和隐性化,信息安全问题成为关键的社会问题.生物特征识别技术是一门利用人类特有的个体特征来验证个人身份的科学,它比传统的基于口令和身份号码的方法更为的可靠.信息融合技术的应用可以大大提高生物特征识别系统的性能.由于指纹识别是最为成熟和流行的生物特征识别之一,本文主要以指纹为例讨论生物特征的多种信息融合方法和相关的主要研究成果.     

概率线性判别分析在语音命令词置信度判决中的应用

置信度判决用于确定语音数据与模型之间的匹配程度,可以发现语音命令系统中的识别错误,提高其可靠性.近年来,基于身份矢量(identity vector,i-vector)以及概率线性判别分析(Probabilistic Linear Discriminant Analysis,PLDA)的方法在说话人识别任务中取得了显著效果.本文尝试将i-vector以及PLDA模型作为一种命令词识别结果置信度分析方法,其无需声学模型、语言模型支撑,且实验表明性能良好.在此基础上,针对i-vector在刻画时序信息方面的不足,尝试将该系统与DTW融合,有效提升了系统对音频时序的鉴别能力.     

生物特征识别技术研究及应用

生物特征识别技术作为一种身份识别的手段,具有独特的优势,对信息安全具有重要意义,近年来已逐渐成为国际上的研究热点。本文介绍了生物特征的概念及多种常见生物特征识别技术,对不同的识别方法的原理、特征及性能做了较详细的分析与评价。     

生物特征识别中的信息融合技术

1 INTRODUCTION Biometrics refers to the automated methods of rec- ognizing a person based on a physiological or behavioral characteristic, and has been the emerging technology during the past decade. Any human physiological or be- havioral characteristic that is universal, unique, perma- nent, and collectable could be used as a biometric. Physical biometrics includes fingerprints, hand or palm geometry, and retina, iris, or facial characteristics. Be- havioral characteristics, traits that…     

多模态生物特征识别技术进展综述

近年来,生物特征识别已经成为一种最具潜力的身份认证技术之一.主要对多模态生物特征识别技术进行概括和总结,同时也介绍了现存若干用于身份识别和验证的多模态生物特征识别系统.经调查研究显示,多模态生物特征识别技术由于在身份认证和识别过程中考虑了个体的多种生理或行为特征,因而表现出了较单生物特征更高的可靠性和安全性,并已成为生物特征识别技术未来发展的趋势之一.     

Remote User Authentication Using a Voice Authentication System

Banking organizations have been delivering services to consumers for many years. The increase in information access terminals along with the growing use of information sensitive applications such as e-commerce, e-learning, e-banking, and e-healthcare have generated a real requirement of reliable, easy to use, and generally acceptable control methods for confidential and vital information. An accurate automatic personal identification is critical to a wide range of application domains. Traditional personal identification methods (e.g., passwords, PIN) suffer from a number of drawbacks and are unable to satisfy the security requirement of highly inter-connected information society. Biometrics refers to automatic identification of an individual based on her physiological or behavioral traits. To achieve these security measures, the proposed system gives highly secured two-factor authentication system that is based on voice recognition technology.     

Soft-computing methods for robust authentication using soft-biometric data

Biometrics is the measurement of person’s physiological or behavioral characteristics. It enables authentication of a person’s identity using such measurements. Biometric-based authentication is thus becoming increasingly important in computer-based applications because the amount of sensitive data stored in such systems is growing. Particularly challenging is the implementation of biometric-based authentication in embedded computer system applications, because the resources of such systems are scarce. Reliability and performance are two primary requirements to be satisfied in embedded system applications. Single-mode and hard-feature-based biometrics do not offer enough reliability and performance to satisfy such requirements. Multimode biometrics is a primary level of improvement. Soft-biometric features can thus be considered along with hard-biometric features to further improve performance. A combination of soft-computing methods and soft-biometric data can yield more improvements in authentication performance by limiting requirements for memory and processing power. The multi-biometric approach also increases system reliability, since most embedded systems can capture more than one physiological or behavioral characteristic. A multi-biometric platform that combines voiceprint and fingerprint authentication was developed as a reference model to demonstrate the potential of soft-computing methods and soft-biometric data. Hard-computing pattern-matching algorithms were applied to match hard-biometric features. Artificial neural network (ANN) processing was applied to match soft-biometric features. Both hard-computing and soft-computing matching results are inferred by a fuzzy logic engine to perform smart authentication using a decision-fusion paradigm. The embedded implementation was based on a single-chip, floating-point, digital signal processor (DSP) to demonstrate the practical embeddability of such an approach and the improved performance that can be attained despite limited system resources.     

Biometric perils and patches

Biometrics authentication offers many advantages over conventional authentication systems that rely on possessions or special knowledge. With conventional technology, often the mere possession of an employee ID card is proof of ID, while a password potentially can be used by large groups of colleagues for long times without change. The fact that biometrics authentication is non-repudiable (hard to refute) and, yet, convenient, is among its most important advantages. Biometrics systems, however, suffer from some inherent biometrics-specific security threats. These threats are mainly related to the use of digital signals and the need for additional input devices, though we also discuss brute-force attacks of biometrics systems. There are also problems common to any pattern recognition system. These include “wolves” and “lambs”, and a new group we call “chameleons”. An additional issue with the use of biometrics is the invasion of privacy because the user has to enroll with an image of a body part. We discuss these issues and suggest some methods for mitigating their impact.     

Biometrics and the threat to civil liberties

Biometrics refers to the automatic identification or verification of living persons using their enduring physical or behavioral characteristics. Biometric systems fall into two categories: authentication and identification, with authentication systems being far more common. We discuss about the Biometric system and its use in authentication and identification. We also discuss about the face recognition system and the threat caused on the civil liberties due to illegal modification of the databases in the biometric system.