Java-based Internet biometric authentication system

An online biometric verification system for use over the Internet and requiring no specialist equipment is presented. Combining two distinct tests to ensure authenticity, a typing style test and a mouse-based signature test, achieves a fraudulent access rate of /spl ap/ 4.4 percent, while authentic users access with a rate of /spl ap/ 99 percent.     

Performance generalization in biometric authentication using joint user-specific and sample bootstraps

Biometric authentication performance is often depicted by a detection error trade-off (DET) curve. We show that this curve is dependent on the choice of samples available, the demographic composition and the number of users specific to a database. We propose a two-step bootstrap procedure to take into account the three mentioned sources of variability. This is an extension to the Bolle et al.'s bootstrap subset technique. Preliminary experiments on the NIST2005 and XM2VTS benchmark databases are encouraging, e.g., the average result across all 24 systems evaluated on NIST2005 indicates that one can predict, with more than 75 percent of DET coverage, an unseen DET curve with eight times more users. Furthermore, our finding suggests that with more data available, the confidence intervals become smaller and, hence, more useful     

基于PKI的生物认证电力安全系统

将PKI技术和生物认证相结合,提出了基于生物证书的身份认证和权限管理的生物认证系统,相对原来单一基于PKI的认证系统具有更高安全性.     

Proposal on a multifactor biometric authentication method based on cryptosystem keys containing biometric signatures

Conventional biometric authentication methods have a serious problem – it is easy to steal the biological information of another person, such as fingerprints, iris patterns, and facial forms. Accordingly, it is essential to protect authentication systems from an attack like impersonation with an artificial finger. To solve this problem, in this paper, we would like to propose a multifactor biometric authentication method based on cryptosystem keys containing biometric signatures. This paper has two new aspects: one is to propose a method for embedding two kinds of data – personal biological information and a confidential item corresponding to a conventional secret key – into a cryptosystem key to detect any impersonation even if the biological information is stolen, and the other is to demonstrate the safety of the method, a factor which has never been referenced. From a privacy protection point of view, we believe that the multifactor biometric authentication method is of great significance. Moreover, it can economically establish certification authorities having the biological information. Meanwhile, in order to uniquely embed the biological information into the cryptosystem key, an algorithm is needed to uniquely select a representative template as described in this paper. In this paper, we propose only the concept of this algorithm and hope that concrete methodologies will be studied in the future.     

A 3D face and hand biometric system for robust user-friendly authentication

A complete authentication system based on fusion of 3D face and hand biometrics is presented and evaluated in this paper. The system relies on a low cost real-time sensor, which can simultaneously acquire a pair of depth and color images of the scene. By combining 2D and 3D facial and hand geometry features, we are able to provide highly reliable user authentication robust to appearance and environmental variations. The design of the proposed system addresses two basic requirements of biometric technologies: dependable performance under real-world conditions along with user convenience. Experimental evaluation on an extensive database recorded in a real working environment demonstrates the superiority of the proposed multimodal scheme against unimodal classifiers in the presence of numerous appearance and environmental variations, thus making the proposed system an ideal solution for a wide range of real-world applications, from high-security to personalization of services and attendance control.     

Multimodal biometric crypto system for human authentication using ear and palm print

Pattern Analysis and Applications - The fuzzy vault structure, which is a biometric pattern safety process in which the biometric traits are represented as an unordered group, is an example of a...     

Bayesian adaptation for user-dependent multimodal biometric authentication

A novel score-level fusion strategy based on Bayesian adaptation for user-dependent multimodal biometric authentication is presented. In the proposed method, the fusion function is adapted for each user based on prior information extracted from a pool of users. Experimental results are reported using on-line signature and fingerprint verification subsystems on the MCYT real bimodal database. The proposed scheme outperforms both user-independent and user-dependent standard approaches. As compared to non-adapted user-dependent fusion, relative improvements of 80% and 55% are obtained for small and large training set sizes, respectively.     

Secure biometric template generation for multi-factor authentication

In the light of recent security incidents, leading to compromise of services using single factor authentication mechanisms, industry and academia researchers are actively investigating novel multi-factor authentication schemes. Moreover, exposure of unprotected authentication data is a high risk threat for organizations with online presence. The challenge is how to ensure security of multi-factor authentication data without deteriorating the performance of an identity verification system? To solve this problem, we present a novel framework that applies random projections to biometric data (inherence factor), using secure keys derived from passwords (knowledge factor), to generate inherently secure, efficient and revocable/renewable biometric templates for users? verification. We evaluate the security strength of the framework against possible attacks by adversaries. We also undertake a case study of deploying the proposed framework in a two-factor authentication setup that uses users? passwords and dynamic handwritten signatures. Our system preserves the important biometric information even when the user specific password is compromised – a highly desirable feature but not existent in the state-of-the-art transformation techniques. We have evaluated the performance of the framework on three publicly available signature datasets. The results prove that the proposed framework does not undermine the discriminating features of genuine and forged signatures and the verification performance is comparable to that of the state-of-the-art benchmark results.     

BiolD: a multimodal biometric identification system

Biometric identification systems, which use physical features to check a person's identity, ensure much greater security than password and number systems. Biometric features such as the face or a fingerprint can be stored on a microchip in a credit card, for example. A single feature, however, sometimes fails to be exact enough for identification. Another disadvantage of using only one feature is that the chosen feature is not always readable. Dialog Communication Systems (DCS AG) developed BioID, a multimodal identification system that uses three different features-face, voice, and lip movement-to identify people. With its three modalities, BioID achieves much greater accuracy than single-feature systems. Even if one modality is somehow disturbed-for example, if a noisy environment drowns out the voice-the ether two modalities still lead to an accurate identification. This article goes into detail about the system functions, explaining the data acquisition and preprocessing techniques for voice, facial, and lip imagery data. The authors also explain the classification principles used for optical features and the sensor fusion options (the combinations of the three results-face, voice, lip movement-to obtain varying levels of security)     

开放网络中的一种生物认证应用框架

提出了一种开放网络环境中进行生物认证的应用框架。本框架中,具有不同功能、分布在不同地点的生物处理单元（BPU）协同完成身份认证后,各自生成包含BPU证书、功能报告以及处理结果的Instance,将其发送到身份验证者处进行验证。身份验证者结合用户属性证书的授权,根据生物策略证书中规定的授权与认证策略的对应关系,决定是否接受本次认证结果。     

Adaptive fuzzy genetic algorithm for multi biometric authentication

Multimedia Tools and Applications - Biometric Authentication (BA) has turn out to be presently as key problem in privacy and security. Multimodal biometric system specializes in enhancing...     

Spatiotemporal analysis of human activities for biometric authentication

This paper presents a novel framework for unobtrusive biometric authentication based on the spatiotemporal analysis of human activities. Initially, the subject’s actions that are recorded by a stereoscopic camera, are detected utilizing motion history images. Then, two novel unobtrusive biometric traits are proposed, namely the static anthropometric profile that accurately encodes the inter-subject variability with respect to human body dimensions, while the activity related trait that is based on dynamic motion trajectories encodes the behavioral inter-subject variability for performing a specific action. Subsequently, score level fusion is performed via support vector machines. Finally, an ergonomics-based quality indicator is introduced for the evaluation of the authentication potential for a specific trial. Experimental validation on data from two different datasets, illustrates the significant biometric authentication potential of the proposed framework in realistic scenarios, whereby the user is unobtrusively observed, while the use of the static anthropometric profile is seen to significantly improve performance with respect to state-of-the-art approaches.     

Cancelable features using log-Gabor filters for biometric authentication

Wide spread use of biometric based authentication requires security of biometric data against identity thefts. Cancelable biometrics is a recent approach to address the concerns regarding privacy of biometric data, public confidence, and acceptance of biometric systems. This work proposes a template protection approach which generates revocable binary features from phase and magnitude patterns of log-Gabor filters. Multi-level transformations are applied at signal and feature level to distort the biometric data using user specific tokenized variables which are observed to provide better performance and security against information leakage under correlation attacks. A thorough analysis is performed to study the performance, non-invertibility, and changeability of the proposed approach under stolen token scenario on multiple biometric modalities. It is revealed that generated templates are non-invertible, easy to revoke, and also deliver good performance.     

Discriminative multimodal biometric authentication based on quality measures

A novel score-level fusion strategy based on quality measures for multimodal biometric authentication is presented. In the proposed method, the fusion function is adapted every time an authentication claim is performed based on the estimated quality of the sensed biometric signals at this time. Experimental results combining written signatures and quality-labelled fingerprints are reported. The proposed scheme is shown to outperform significantly the fusion approach without considering quality signals. In particular, a relative improvement of approximately 20% is obtained on the publicly available MCYT bimodal database.     

Dynamic sample size detection in learning command line sequence for continuous authentication

Continuous authentication (CA) consists of authenticating the user repetitively throughout a session with the goal of detecting and protecting against session hijacking attacks. While the accuracy of the detector is central to the success of CA, the detection delay or length of an individual authentication period is important as well since it is a measure of the window of vulnerability of the system. However, high accuracy and small detection delay are conflicting requirements that need to be balanced for optimum detection. In this paper, we propose the use of sequential sampling technique to achieve optimum detection by trading off adequately between detection delay and accuracy in the CA process. We illustrate our approach through CA based on user command line sequence and na?ve Bayes classification scheme. Experimental evaluation using the Greenberg data set yields encouraging results consisting of a false acceptance rate (FAR) of 11.78% and a false rejection rate (FRR) of 1.33%, with an average command sequence length (i.e., detection delay) of 37 commands. When using the Schonlau (SEA) data set, we obtain FAR = 4.28% and FRR = 12%.     

Adapted user-dependent multimodal biometric authentication exploiting general information

A novel adapted strategy for combining general and user-dependent knowledge at the decision level in multimodal biometric authentication is presented. User-independent, user-dependent, and adapted fusion and decision schemes are compared by using a bimodal system based on fingerprint and written signature. The adapted approach is shown to outperform the other strategies considered in this paper. Exploiting available information for training the fusion function is also shown to be better than using existing information for post-fusion trained decisions.     

基于特征融合的多模态身份识别方法研究

研究了多模态身份识别问题,结合人脸和掌纹两种不同生理特征,提出了基于特征融合的多模态身份识别方法。对人脸和掌纹图像分别进行Gabor小波、二维主元变换(2DPCA)提取图像特征,根据新的权重算法,结合两种模态的特征,利用最邻近分类器进行分类识别。在AMP、ORL人脸库和Poly-U掌纹图像库中的实验结果表明,两种模态的融合能更多地给出决策分析所需的特征信息相比传统的单一模态的人脸或掌纹识别具有较高的识别率,更具安全性和准确性。     

Model for adaptable context-based biometric authentication for mobile devices

It becomes possible to take advantage of seamless biometric authentication on mobile devices due to increasing quality and quantity of built-in sensors, increasing processing power of the devices, and wireless connectivity. However, practical effectiveness of the biometric authentication application depends on user’s environment conditions that can decrease the accuracy of biometrics recognition or make the acquisition process undesirable for mobile user in a given moment, i.e., effectiveness depends on usage context. In this paper, context-based biometric authentication model for mobile devices is proposed. It enables determining the most accurate authentication method at the moment along with the most accurate form of interacting with a user w.r.t. authentication process. The generic model designed and verified with proof-of-concept implementation constitutes a foundation for building further adaptable and extensible multi-factor context-dependent systems for mobile authentication.     

Bi-modal biometric authentication on mobile phones in challenging conditions

This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone. To perform these experiments we develop three novel authentication protocols for the large publicly available MOBIO database. We evaluate state-of-the-art face, speaker and bi-modal authentication techniques and show that inter-session variability modelling using Gaussian mixture models provides a consistently robust system for face, speaker and bi-modal authentication. It is also shown that multi-algorithm fusion provides a consistent performance improvement for face, speaker and bi-modal authentication. Using this bi-modal multi-algorithm system we derive a state-of-the-art authentication system that obtains a half total error rate of 6.3% and 1.9% for Female and Male trials, respectively.     

Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems

We examine the performance of multimodal biometric authentication systems using state-of-the-art commercial off-the-shelf (COTS) fingerprint and face biometric systems on a population approaching 1,000 individuals. The majority of prior studies of multimodal biometrics have been limited to relatively low accuracy non-COTS systems and populations of a few hundred users. Our work is the first to demonstrate that multimodal fingerprint and face biometric systems can achieve significant accuracy gains over either biometric alone, even when using highly accurate COTS systems on a relatively large-scale population. In addition to examining well-known multimodal methods, we introduce new methods of normalization and fusion that further improve the accuracy.