结合并行融合的序列化多模态生物特征识别系统框架

针对多模态生物特征识别系统并行融合模式中使用方便性和使用效率方面的问题,在现有序列化多模态生物特征识别系统的基础上,提出了一种结合并行融合和序列化融合的多生物特征识别系统框架。框架中首先采用步态、人脸与指纹三种生物特征的不同组合方式以加权相加的得分级融合算法进行的识别过程;其次,利用在线的半监督学习技术提高弱特征的识别性能,从而进一步增强系统的使用方便性和识别可靠性。理论分析和实验结果表明,在此框架下,随使用时间的推移,系统能够通过在线学习提高弱分类器的性能,用户的使用方便性和系统的识别精度都得到了进一步提升。     

改进证据理论的多生物特征融合方法

多生物特征融合考虑了个体的多种生理或行为特征,因而能显著地改善系统的识别性能,成为生物特征识别技术未来发展趋势之一。利用训练样本的识别率和误识率,提出了基于证据理论的多生物特征融合识别方法;对各识别专家的识别率和误识率进行分析,提出了一种基于累积频率和证据理论（Cumulative Frequency based D-S,CFDS）的多生物特征融合方法;通过几个实验证明了改进的D-S算法的有效性,提高了合成结果的可靠性。     

基于SEAL的虹膜特征密文认证系统北大核心CSCD

随着生物识别技术的应用和推广,生物特征对身份认证的影响愈加显著。为了保证用户的隐私,生物特征不能以明文形式进行存储或操作。针对此问题,文章对现有的生物特征认证系统的方案、性能做了分析和总结,采用FV方案构建并设计了一个基于全同态加密的虹膜特征密文认证系统。实现部分借助了微软的SEAL (Simple Encrypted Arithmetic Library)库。整个系统可在不对虹膜特征模板解密的情况下完成虹膜认证,且数据库中保存的是虹膜特征模板的同态密文,所以无需担心虹膜特征模板的泄露。同时该系统无需可信中心进行验证,直接通过一次性MAC认证方法在服务器端完成认证。测试表明,当系统采用海明距离比对算法等计算电路深度不高的虹膜算法时,有着不错的性能,基本满足了真实应用场景的需求。     

基于改进ENN算法的多生物特征融合的身份验证

基于多生物特征的身份鉴别技术已受到越来越多的重视.单个生物特征有其固有的局限性,通过融合不同的生物特征可以提高身份鉴别系统的验证性能和鲁棒性.该文融合了声纹和指纹特征,提出了一种改进的ENN方法,并与K-NN、传统ENN方法进行了比较.改进的ENN将认证率提高了大约2％.同时,又在不同的数据集上比较了改进的ENN方法和基于Bayes理论的融合系统,分析并评价了两种方法的适用范围和优缺点.实验结果证明了此方法的有效性.     

基于生物密钥系统的能量攻击分析

为评估生物密钥系统在侧信道攻击下的安全性能,在分析生物密钥系统结构和特点的基础上,将用户的击键生物特征和秘密共享方案相结合,设计一个基于击键的安全生物密钥系统,并通过差分能量攻击技术测量安全生物密钥系统的功耗泄漏。仿真分析表明,攻击者借助少量的功耗泄露就可以破解生物密钥系统的信息。     

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。     

基于欧氏权距离法的多生物特征身份鉴别

通过融合不同的生物特征可以改进身份鉴别系统的验证性能。本文借鉴多传感器信息融合技术中聚类分析的思想,提出了基于欧氏权距离法的多生物特征融合的身份鉴别方法。该方法并不局限于特定的两类或多类生物特征,同时该方法克服了传统的估计方法和统计方法需要先验概率和概率密度函数的缺点,更加符合实际应用的需要。     

基于区块链技术的生物特征和口令双因子跨域认证方案

为解决传统跨域认证方式不多且方案复杂的问题,提出了基于区块链技术的生物特征和口令双因子跨域认证方案。首先,使用模糊提取技术提取生物特征的随机密钥参与认证,解决了生物特征泄露导致永久不可用的问题;其次,利用不易篡改的区块链存储生物特征公开信息,解决了模糊提取技术易受主动攻击威胁的问题;最后,基于区块链的分布式存储功能与联盟链架构,实现了用户在本地和异地环境下的双因子跨域认证。安全性分析和效率分析的结果表明,在安全性方面,所提方案具有抗中间人攻击、抗重放攻击等安全属性;在效率与可用性方面,该方案效率适中,用户无需携带智能卡,系统的可扩展性强。     

基于神经网络融合的语音人脸身份识别方法

传统的身份识别系统利用单一的生物特征作为依据,在复杂背景下,系统性能往往会大幅下降。基于数据融合的多生物特征身份识别技术可以提高生物识别系统的准确率等性能。该文利用特征脸和矢量量化方法建立人脸识别和语音识别两个子系统,在决策层用神经网络融合子系统的输出来进行身份识别。实验证明该方法比单个子系统识别率高,在噪音环境下,优势明显。     

人物脸部图片的修改与实现*

包括人脸表情修改在内的人体生物特征的操作技术,涉及的脸部图像处理是图像处理学科的技术难点.研究了影响人脸识别技术实用化的一些因素,提出了开发人脸识别与修改技术的方法,开发出一个人脸图像处理系统,主要用于图片中人脸部位的变换与修改,为今后开发性能优良的人脸识别与修改系统奠定基础.     

n-挠群上基于多重生物特征身份的签名方案

针对基于单一生物特征身份的签名方案在实际应用中存在的问题,提出一种基于多重生物特征身份的签名方案,研究基于椭圆曲线的n-挠群,对不同生物特征进行融合,介绍基于生物特征身份的公钥密码体制,仿真实验结果表明,该方案在安全性、稳定性以及可靠性等方面,均具有一定优越性。     

Genetic programming for multibiometrics

Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture… One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature extraction algorithms, multiple biometric modalities…). In this paper, we are interested in score level fusion functions application (i.e., we use a multibiometric authentication scheme which accept or deny the claimant for using an application). In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) provided by different biometric systems provide one of the best performances. We present a new method based on the use of genetic programming giving similar or better performances (depending on the complexity of the database). We derive a score fusion function by assembling some classical primitives functions (+, ∗, −, … ). We have validated the proposed method on three significant biometric benchmark datasets from the state of the art.     

Feature selection for support vector machine-based face-iris multimodal biometric system

Multimodal biometric can overcome the limitation possessed by single biometric trait and give better classification accuracy. This paper proposes face-iris multimodal biometric system based on fusion at matching score level using support vector machine (SVM). The performances of face and iris recognition can be enhanced using a proposed feature selection method to select an optimal subset of features. Besides, a simple computation speed-up method is proposed for SVM. The results show that the proposed feature selection method is able improve the classification accuracy in terms of total error rate. The support vector machine-based fusion method also gave very promising results.     

A cascaded deep learning framework for iris centre localization in facial image

Accurate iris centre localization is crucial in many computer vision and facial biometric applications such as gaze estimation, human–computer interaction, iris recognition, and liveness detection. However, it is challenging in an uncontrolled environment due to variations like pose, scale, rotation, specular reflection, and image quality. Therefore, a cascaded deep learning framework for iris centre localization in facial images is proposed that is robust to the abovementioned variations. The proposed approach consists of (i) YOLOv3 for eye detection, (ii) UNet for iris segmentation, and (iii) statistical modelling for iris centre localization. The eyes are first detected using the YOLOv3, and subsequently, iris segmentation is performed within the detected eyes using the UNet. Following iris segmentation, statistical modelling is employed to enhance the localization accuracy of the iris centre. Experiments were performed on benchmark databases, resulting in a standardized error measure S_ED of 3.405 pixels for BioID and 3.259 pixels for GI4E databases. In addition, the robustness of the proposed eye detection model was further evaluated on the Yale B for illumination variations and the CAS-PEAL for pose variations.     

Fusion of physiological measures for multimodal biometric systems

Physiological measures are widely studied from a medical point of view. Most applications lie in the field of diagnosis of heart attacks, as regards the ECG, or the detection of epileptic events, in the case of the EEG. In the last ten years, these signals are being investigated also from a biometric point of view, in order to exploit the discriminative capability provided by these measures in recognizing individuals. The present work proposes a multimodal biometric recognition system based on the fusion of the first lead (i) of the electrocardiogram (ECG) with six different bands of the electroencephalogram (EEG). The proposed approach is based on the extraction of fiducial features (peaks) from the ECG combined with spectrum features of the EEG. A dataset has been created, by composing the signals of two well-known databases. The results, reported by means of EER values, AUC values and ROC curves, show good recognition performances.     

Computational Intelligence-Based Biometric Technologies

Computational intelligence (CI) technologies are robust, can be successfully applied to complex problems, are efficiently adaptive, and usually have a parallel computational architecture. For those reasons they have been proved to be effective and efficient in bio-metric feature extraction and biometric matching tasks, sometimes used in combination with traditional methods. In this article, we briefly survey two kinds of major applications of CI in biometric technologies, CI-based feature extraction and CI-based biometric matching. Varieties of evolutionary computation and neural networks techniques have been successfully applied to biometric data representation and dimensionality reduction. CI-based methods, including neural network and fuzzy technologies, have also been extensively investigated for biometric matching. CI-based biometric technologies are powerful when used in the representation and recognition of incomplete biometric data, discriminative feature extraction, biometric matching, and online template updating, and promise to have an important role in the future development of biometric technologies     

A review on performance,security and various biometric template protection schemes for biometric authentication systems

Identifying a person based on their behavioral and biological qualities in an automated manner is called biometrics. The authentication system substituting traditional password and token for authentication and relies gradually on biometric authentication methods for verification of the identity of an individual. This proves the fact that society has started depending on biometric-based authentication systems. Security of biometric authentication needs to be reviewed and discussed as there are multiple points related to integrity and public reception of biometric-based authentication systems. Security and recognition accuracy are the two most important aspects which must be considered while designing biometric authentication systems. During enrollment phase scanning of biometric data is done to determine a set of distinct biometric feature set known as biometric template. Protection of biometric templates from various hacking efforts is a topic of vital importance as unlike passwords or tokens, compromised biometric templates cannot be reissued. Therefore, giving powerful protection techniques for biometric templates and still at that very moment preparing great identification accuracy is a good research problem nowadays, as well as in the future. Furthermore, efficiency under non-ideal conditions is also supposed to be inadequate and thus needs special attention in the design of a biometric authentication system. Disclosure of various biometric traits in miscellaneous applications creates a severe compromise on the privacy of the user. Biometric authentication can be utilized for remote user authentication. In this case, the biometric data of users typically called templates are stored in a server. The uniqueness and stability of biometrics ended it useful over traditional authentication systems. But, a similar thing made the enduring harm of a user’s identity in biometric systems. The architecture of the biometric system leads to several hazards that lead to numerous security concerns and privacy threats. To address this issue, biometric templates are secured using several schemes that are categorized as biometric cryptosystems, cancelable biometrics, hybrid methods, Homomorphic Encryption, visual cryptography based methods. Biometric cryptosystems and cancelable biometrics techniques provide reliable biometric security at a great level. However, there persist numerous concerns and encounters that are being faced during the deployment of these protection technologies. This paper reviews and analyses various biometric template protection methods. This review paper also reflects the limitations of various biometric template protection methods being used in present times and highlights the scope of future work.

     

Biometric template protection using cancelable biometrics and visual cryptography techniques

Wide spread use of biometric based authentication implies the need to secure biometric reference data. Various template protection schemes have been introduced to prevent biometric forgery and identity thefts. Cancelable biometrics and visual cryptography are two recent technologies introduced to address the concerns regarding privacy of biometric data, and to improve public confidence and acceptance of biometric systems. Cancelable biometrics is an important technique that allows generation of revocable biometric templates. As the number of biometric instances are limited and once compromised they are lost forever. Cancelable biometrics allows templates to be cancelled and revoked like passwords innumerable times. Recently, various approaches that utilize visual cryptography to secure the stored template and impart privacy to the central databases have been introduced. This work attempts to summarize the existing approaches in literature making use of these two technologies to protect biometric templates.     

Multi-instance cancellable biometrics schemes based on generative adversarial network

The main role of cancellable biometric schemes is to protect the privacy of the enrolled users. The protected biometric data are generated by applying a parametrized transformation function to the original biometric data. Although cancellable biometric schemes achieve high security levels, they may degrade the recognition accuracy. One of the mostwidely used approaches to enhance the recognition accuracy in biometric systems is to combine several instances of the same biometric modality. In this paper, two multi-instance cancellable biometric schemes based on iris traits are presented. The iris biometric trait is used in both schemes because of the reliability and stability of iris traits compared to the other biometric traits. A generative adversarial network (GAN) is used as a transformation function for the biometric features. The first scheme is based on a pre-transformation feature-level fusion, where the binary features of multiple instances are concatenated and inputted to the transformation phase. On the other hand, the second scheme is based on a post-transformation feature-level fusion, where each instance is separately inputted to the transformation phase. Experiments conducted on the CASIA Iris-V3-Internal database confirm the high recognition accuracy of the two proposed schemes. Moreover, the security of the proposed schemes is analyzed, and their robustness against two well-known types of attacks is proven.

     

多生物特征识别平台的设计与实现

针对目前生物特征识别研究大多基于特定特征而难以集成其它特征的现状，提出了整合多种生物特征识别技术的通用解决方案．通过对生物特征识别研究的需求进行分析，抽取了生物特征识别平台的基本逻辑模型．建立了易于扩展、可复用的生物特征识别平台框架THBio，同时在平台中进一步整合了生物特征识别评测厦多生物特征融合的功能，为提高生物特征识别技术的研究水平提供了可能．