基于手机触摸屏传感器多点触摸身份认证算法

针对智能手机所面临的信息安全威胁问题,提出一种基于手机触摸屏传感器的多点触摸身份认证方法。首先由触摸屏传感器采集手指滑动原始数据序列,通过平滑去噪、位置及长度归一化预处理;然后提取手势运动一阶、二阶归一化导数序列及运动方向为身份验证特征序列;最后采用模板匹配方法,使用动态时间规整算法匹配比较注册模板特征序列与测试特征序列,判断用户身份真实性。仿真结果表明,所提算法对不同用户身份认证的平均错误拒绝率和错误接受率分别为3.83%和2.07%,与使用径向基函数为核函数的支持向量分布估计(SVDE)算法相比,平均错误拒绝率和错误接受率分别降低1.81%和2.35%。经性能分析,所提算法能明显提高身份认证的准确性。     

基于三分类的击键序列身份认证

针对基于统计学用户击键模式识别算法识别率较低的不足，提出了一种统计学三分类主机用户身份认证算法。该方法通过对当前注册用户的击键特征与由训练样本得到的标准击键特征进行比较，将当前注册用户划分为合法用户类、怀疑类与入侵类三类,对怀疑类采用二次识别机制。 采用动态判别域值，引入了与系统安全性和友好性相关的可控参量k，由系统管理员根据实际确定。并对该算法性能进行了理论分析与实验测试，结果表明该算法在保持贝叶斯统计算法需要训练样本集规模较小、算法收敛速度快优点的基础上,识别精度高于贝叶斯统计算法，错误拒绝率(FRR)和错误通过率(FAR)分别为1.6%和1.5%。     

多印痕指纹识别的数据融合技术

由于指纹图像中存在非线性变形和噪声干扰,当前的指纹识别系统不能满足某些系统要求保持很低的误接受率(FAR)的同时达到较低误拒绝率(FRR)的要求。提高识别性能的一个有效的方法是融合多个模板和多个指纹的数据融合技术。该文研究的一种多印痕指纹数据融合技术能够改善系统性能。通过实验证明了这种数据融合技术能够有效提高系统性能,可以使系统在保持低误接受率的条件下降低误拒绝率。     

Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture

Touch gesture biometrics authentication system is the study of user's touching behavior on his touch device to identify him. The features traditionally used in touch gesture authentication systems are extracted using hand-crafted feature extraction approach. In this work, we investigate the ability of Deep Learning (DL) to automatically discover useful features of touch gesture and use them to authenticate the user. Four different models are investigated Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN) combined with LSTM (CNN-LSTM), and CNN combined with GRU(CNN-GRU). In addition, different regularization techniques are investigated such as Activity Regularizer, Batch Normalization (BN), Dropout, and LeakyReLU. These deep networks were trained from scratch and tested using TouchAlytics and BioIdent datasets for dynamic touch authentication. The result reported in terms of authentication accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR). The best result we have been obtained was 96.73%, 96.07% and 96.08% for training, validation and testing accuracy respectively with dynamic touch authentication system on TouchAlytics dataset with CNN-GRU DL model, while the best result of FAR and FRR obtained on TouchAlytics dataset was with CNN-LSTM were FAR was 0.0009 and FRR was 0.0530. For BioIdent dataset the best results have been obtained was 84.87%, 78.28% and 78.35% for Training, validation and testing accuracy respectively with CNN-LSTM model. The use of a learning based approach in touch authentication system has shown good results comparing with other state-of-the-art using TouchAlytics dataset.     

单芯片微小型指纹识别系统设计与实现

嵌入式指纹识别产品的推广应用一直受到成本和体积因素的制约。提出一种基于ARM7处理器芯片LPC2106为核心的单芯片嵌入式自动指纹识别系统设计方案,给出了系统组成的电路结构,运用了一种高效率的嵌入式指纹图像拼接及识别算法软件,并设计出用于改善指纹识别性能的指纹引导槽方案。结果表明,2秒内能完成20个指纹用户的识别,在认假率为十万分之一时,拒真率不超过百分之三,达到国家有关标准的要求。该产品体积小,价格只有同类产品的约三分之一,可应用于指纹门锁、指纹保险箱、指纹遥控器等领域。     

手势认证中基于能量熵的端点检测方法研究

为了保证智能手机敏感信息的安全性,设计实现了一种基于手机内置三轴加速度传感器的三维手势认证方案。在手势端点检测部分,在定性分析手势加速度信号能量分布特性的基础上,提出了一种基于能量熵的新方法实现有效手势截取。进一步设计基于欧式距离的动态时间规整算法对截取后的手势序列信号进行匹配认证,当他人模仿手势错误接受率趋近0%时,本人认证手势错误拒绝率维持在7%左右,从而实现智能手机用户身份识别。     

基于遗传算法和灰色关联分析的击键特征识别算法

基于用户击键特征的身份认证比传统的基于口令的身份认证方法有更高的安全性，现有研究方法中基于神经网络、数据挖掘等算法计算复杂度高，而基于特征向量、贝叶斯统计模型等算法识别精度较低。为了在提高识别精度的同时有效降低计算复杂度，在研究现有算法的基础上提出了一种基于遗传算法与灰色关联分析的击键特征识别算法。该算法利用遗传算法根据用户训练样本确定表征用户击键特征的标准特征序列，通过对当前用户击键特征序列与标准特征序列进行灰色关联分析实现用户身份认证。实验结果表明，该算法识别精度达到神经网络、支持向量机等算法的较高水平，错误拒绝率与错误接受率分别为0%与1.5%。且计算复杂度低，与基于特征向量的算法相近。     

On the false rejection ratio of face recognition based on automatic detected feature points

The authors propose a new face recognition system with an evaluation function using feature points. The feature points are detected automatically by Milborrow’s Stasm software. Before recognition, rotation compensation and size normalization are applied to the feature points. The main method is to calculate the squared error between the registered face and the input face as to length of a characteristic pair of feature points on face. The False Rejection Rate (FRR) for the registered and input face of the same person, and the False Acceptance Rate (FAR) for the registered face and a different person’s input face are evaluated. The input is a video sequence. Stable recognition is obtained with small FRR and FAR for the video of a period of 0.5 s.     

基于力场转换理论的耳廓识别

本文提出了一种基于力场转换理论的人耳识别方法,在检测出耳廓边缘的基础上,将图像分别通过力场和能量场进行描述,利用测试点在力场中运动最终收敛至图像能量局部最小值处这一个特征,对人耳图像特征点进行定位,最终利用提取出的“势能阱”和“势能渠”实现匹配与识别,经在选用的耳廓图库上实验,错误接受率FAR为1．28％,错误拒绝率FRR为6．28％。     

LC698S片上指纹算法的优化与应用

主要介绍在无MMU支持的硬件环境中,为了解决程序运行的内存碎片的问题,利用自定义的内存池(Memory Pool)为指纹程序分配内存空间,通过片内RAM实现指纹识别功能的方案.本文对经典的预处理算法以及以特征点的拓扑结构作为特征值的识别算法作了进一步的优化.除此之外,本文提出分辨率动态分布算法,这种算法降低了原图像分辨率,改变了图像大小,节省了片内RAM空间,加快了图像处理速度,使得FAR与FRR达到识别要求,同时缩短指纹识别时间.     

基于SIFT的说话人唇动识别

针对唇部特征提取维度过高以及对尺度空间敏感的问题,提出了一种基于尺度不变特征变换（SIFT）算法作特征提取来进行说话人身份认证的技术。首先,提出了一种简单的视频帧图片规整算法,将不同长度的唇动视频规整到同一的长度,提取出具有代表性的唇动图片;然后,提出一种在SIFT关键点的基础上,进行纹理和运动特征的提取算法,并经过主成分分析（PCA）算法的整合,最终得到具有代表性的唇动特征进行认证;最后,根据所得到的特征,提出了一种简单的分类算法。实验结果显示,和常见的局部二元模式（LBP）特征和方向梯度直方图（HOG）特征相比较,该特征提取算法的错误接受率（FAR）和错误拒绝率（FRR）表现更佳。说明整个说话人唇动特征识别算法是有效的,能够得到较为理想的结果。     

Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

Due to the enormous usage of the internet for transmission of data over a network, security and authenticity become major risks. Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server. To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical (Face, fingerprint, Ear etc.) and behavioural (Gesture, Voice, tying etc.) by means of matching and verification process. In this work, biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data. The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates. Here Face Attribute Convolutional Neural Network (FACNN) is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing. Implementing Artificial Intelligence (AI) into the proposed system, automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing. Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification. Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity. This system enhances the system performance by 8% matching and verification and minimizes the False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) by 6 times and increases the data privacy through the biometric cryptosystem by 98.2% while compared to other work.     

基于流形学习的用户身份认证

本文基于等距映射（ISOMAP）非线性降维算法, 提出了一种新的基于用户击键特征的用户身份认证算法, 该算法用测地距离代替传统的欧氏距离, 作为样本向量之间的距离度量,在用户击键特征向量空间中挖掘嵌入的低维黎曼流形,进行用户识别。用采集到的1500个击键模式数据进行实验测试,结果表明,该文的算法性能优于现有的同类算法,其错误拒绝率（FRR）和错误通过率（FAR）分别是1.65%和0%,低于现有的同类算法。     

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%.     

An approach for on-line signature authentication using Zernike moments

In this work, shape analysis of the acceleration plot, using lower order Zernike moments is performed for authentication of on-line signature. The on-line signature uses time functions of the signing process. The lower order Zernike moments represent the global shape of a pattern. The derived feature, acceleration vector is computed for the sample signature which comprises on-line pixels. The Zernike moment represent the shape of the acceleration plot. The summation value of a Zernike moment for a signature sample is obtained on normalized acceleration values. This type of substantiation decreases the influence of primary features with respect to translation, scaling and rotation at preprocessing stage. Zernike moments provide rotation invariance. In this investigation it was evident that the summation of magnitude of a Zernike moment for a genuine sample was less as compared to the summation of magnitude of a imposter sample. The number of derivatives of acceleration feature depends on the structural complexity of the signature sample. The computation of best order by polynomial fitting and reference template of a subject is discussed. The higher order derivatives of acceleration feature are considered. Signatures with higher order polynomial fitting and complex structure require higher order derivatives of acceleration. Each derivative better represents a portion of signature. The best result obtained is 4% of False Rejection Rate [FRR] and 2% of False Acceptance Rate [FAR].     

Efficient yet robust biometric iris matching on smart cards for data high security and privacy

Smart control access to any service and/or critical data is at the very basis of any smart project. Biometrics have been used as a solution for system access control, for many years now. However, the simple use of biometrics cannot be considered as final and perfect solution. Most problems are related to the data transmission method between the medias, where the users require access and the servers where the biometric data, captured upon registration, are stored. In this paper, we use smart cards as an effective yet efficient solution to this critical data storage problem. Furthermore, iris texture has been used as a human identifier for some time now. This biometric is considered one of the most reliable to distinguish a person from another as its unique yet perfectly stable over time. In this work, we propose an efficient implementation of iris texture verification on smart cards. For this implementation, the matching is done on-card. Thus, the biometric characteristics are always kept in the owner’s card, guaranteeing the maximum security and privacy. In a first approach, the False Acceptance Rate (FAR) and False Rejection Rate (FRR) are improved using circular translations of the matched iris codes. However, after a thorough analysis of the achieved results, we show that the proposed method introduces a significant increase in terms of execution time of the matching operation. In order to mitigate this impact, we augmented the proposed technique with acceptance threshold verification, thus decreasing drastically the execution time of the matching operation, and yet achieving considerably low FAR and FRR. It is noteworthy to point out that these characteristics are at the basis of any access control successful usage.     

一种面向击键动态身份认证的多模板选择算法

在击键动态身份认证系统中,样本采集和模板建立直接影响系统性能。目前单模板击键认证系统存在无法使错误接受率和错误拒绝率都降低到可接受范围内的不足。为此将多模板思想引入击键认证过程中,在提出最大认证概率算法和最小认证概率算法后,提出均衡概率多模板选择算法,将两种错误率都控制在合理范围内。通过实验同GMMS算法进行对比,并研究了模板数和模板样本数对认证结果的影响,最后与单模板认证系统进行了比较分析。     

An asymmetric fingerprint matching algorithm for Java Card TM

We propose a light-weight fingerprint matching algorithm that can be executed inside the devices with a limited computational power. The algorithm is based on the minutiae local structures (the “neighborhoods”), that are invariant with respect to global transformations like translation and rotation. The match algorithm has been implemented inside a smartcard over the Java Card^TM platform, meeting the individual’s need for information privacy and overall authentication procedure security. The main characteristic of the algorithm is to have an asymmetric behavior, in respect to the execution time, between correct positive and negative matches. The performances in terms of authentication reliability and speed were tested on some databases from the Fingerprint Verification Competition 2002 and 2004 editions (FVC2002 and FVC2004). Moreover, our procedure showed better reliability when compared with a related algorithm on the same database. We can achieve a false acceptance rate (FAR) of 0.1%, a false rejection rate of about 6%, and from 0.3 to 8 s to match most of the finger pairs during the FAR tests.     

具有普适性的改进非负矩阵分解图像特征提取方法

为提高图像特征提取的普适性,提出了一种基于改进非负矩阵分解（NMF）的图像特征提取方法。首先,考虑到提取的图像特征的实际意义,选用非负矩阵分解模型进行图像特征的降维处理;其次,为实现用较小数量系数来描述图像特征,将稀疏约束作为非负矩阵分解模型的正则项之一;然后,为使降维后优化得到的特征具有较好的类间区分性,将聚类属性作为非负矩阵分解的另一个正则项;最后,通过对模型的梯度下降优化求解,获得最优的特征基向量与图像特征向量。实验结果表明,针对3种图像数据库,所提的图像特征更有利于图像正确分类或识别,错误接受率（FAR）与错误拒绝率（FRR）分别可以降低到0.021与0.025。     

Evaluation on a keystroke authentication system by keying force incorporated with temporal characteristics of keystroke dynamics

This paper presents the study to develop and evaluate techniques to authenticate valid users, using the keystroke dynamics of a user's PIN number entry on a numerical keypad, with force sensing resistors. Added with two conventional parameter lists of elements, i.e. digraph latency times and key hold times, keying force was chosen as a third element. Two experiments were conducted. The first experiment was to evaluate whether the three types of elements derived from keystrokes have a significant effect for subjects and to examine how trials and session effects generated the variation of the three elements. The second experiment was to demonstrate the system performance by calculating the False Rejection Rate (FRR) and the False Acceptance Rate (FAR) of the system. In the second experiment, a total of 20 keystrokes were recorded from each subject one week after the memorizing session, in order to evaluate the FRR of the system. To evaluate the FAR of the system, the subjects pretended to be impostors, and therefore they repeatedly watched videotaped pass trials made by a valid user as many times as they desired, and tried to imitate the keystroke dynamics of the valid users. The subject's keystrokes were then evaluated on whether they could fool the system. The first experiment, ANOVA revealed that a significant effect of subject was found on each of all three elements. Trial was not significantly affected to digraph latency times and peak force; however, it was significantly affected to key hold times. There was a trend that keystroke dynamics characterized by each element showed reformation of their patterns and reached a steady state over the 10 weeks of experimental sessions. The results of the second experiment showed the average equal error rate to be 2.4%. The results of system performance were compared with those of other studies and concluded that it was difficult to obtain enough information to behave as a perfect impostor by monitoring the videotaped keystrokes.