基于段匹配差异观察值的HMM在线签名认证方法研究

提出一种用签名的分段差异值作为隐马尔可夫模型(HMM)观测值的在线签名认证应用方法。首先,采用双向后向合并DTW算法确定签名中关键点之间的对应关系。然后,采用经典DTW度量签名中各种细微的差异,用这些DTW差异值作为观测值训练HMM模型。将模型状态的意义定义为相似程度,将状态转移结构设定为全概率转移。在SVC2004签名数据库上,验证了该方法的有效性。     

基于HMM的在线手写签名认证系统设计与实现

在线手写签名认证是以人的行为特征为基础的身份认证技术.对在线手写签名字型曲线进行分段,分析了一系列特征,并将隐马尔可夫模型(HMM)引入到在线手写签名认证中.找到了真签名中某种比较稳定的特征,提出了一种基于隐马尔可夫模型的在线手写签名认证方法.从实验数据来看,取得了比较满意的认证效果.     

基于支持向量数据描述的在线签名认证系统

提出一种基于支持向量数据描述方法的在线签名身份认证系统。首先,采用双向后向合并DTW算法确定签名中关键点之间的对应关系,然后采用经典DTW度量签名局部中各种细微的差异。文中提出基于差异值均值方差最小原则的特征选择方法。最后,采用支持向量数据描述方法设计分类器。为得到更好的认证效果,采用多层交叉验证和遗传算法寻找最优的分类器参数。在SVC2004数据库上,系统对熟练伪造签名取得4。25%的平均等错误率。     

基于笔段特征和HMM的在线签名认证方法研究

手写签名过程中的书写力包含丰富个人特征.受输入设备所限,目前的在线手写签名认证系统都没有能够充分利用这一信息.基于此,本文提出一种综合利用签名字形和书写力特征的在线签名认证方法.首先,利用一种新型的F-Tablet手写板用于获取签名的动态笔迹和书写力信息,并根据速度极小值点对签名进行笔画分段.然后从中提取16维的字形和书写力特征矢量序列用于签名隐马尔可夫模型(HMM)的建立和认证.在基于F-Tablet手写板建立的签名数据库上的认证实验结果表明书写力特征比字形特征更难以模仿,两种特征相结合可以有效提高系统的识别性能,提出的认证方法的相等错误率(EER)达到3.9%.     

改进VDDTW的在线手写签名认证算法

在线手写签名认证是一种基于生物特征的身份认证技术。将VDDTW算法应用于在线手写签名认证,该算法改进了DTW中局部匹配距离的计算方法,考虑了时间序列局部曲线的变化趋势,使得时间序列的局部点到点的对正更加合理。在采用有训练的伪造样本的情况下,对累积匹配距离进行时间加权,加大了真伪签名的区分度。实验结果表明了VDDTW算法用于在线签名认证的有效性。     

DTW在线手写签名认证系统的研究

随着计算机网络技术和生物识别技术的发展,通过生物特征进行身份认证成为信息时代的需要.由于长久以来作为授权标志,手写签名是最容易被大众接受的身份认证方式之一.本文改进了经典DTW中距离的计算方法,在考虑了时间序列自身形状特征的基础上,解决了在线手写签名中时间序列非线性弯曲的对正问题.经过签名认证实验表明,当ERR为3%时,签名认证的效果最好,进而证明在线手写签名适合作为身份认证手段.     

基于DTW的注意力机制BLSTM在线手写签名认证

为了提高在线手写签名认证的准确率，设计了一种挖掘签名稳定笔段用于认证的方法.本文提出对签名笔段计算累计差异值矩阵进行匹配；其次采用动态时间规划(DTW)算法计算笔段稳定度；在此基础上，通过以笔段的特征输入双向长短期记忆网络(BLSTM)加注意机制进行处理，从而得到每个用户的稳定签名段集合；最后提取该集合的特征进行分类.该方法在svc2004数据库上进行验证并得到了97.08%的认证率，并在40个用户上取得了1.16%的等误率.该结果表明本文方法能够提高认证精度，并且验证了BLSTM与稳定笔段结合方法的有效性.     

隐马尔可夫模型下视频手写签名认证算法研究

为了提高视频手写签名认证的准确率,确保身份认证的严谨性,需要对隐马尔可夫(Hidden Markov Models,HMM)模型下视频手写签名认证算法进行改进设计;使用当前算法对视频手写签名进行认证时,存在认证判断误差大、效率低的问题;提出基于HMM模型下视频手写签名认证算法;该算法将使用Wacom手写板采集手写签名特征点及压力数据,对采集到的手写签名特征与压力数据进行预处理,消除采集过程中环境和手写板产生的影响,并且规范采集的视频手写签名特征位置、尺寸,提取手写签名特征与压力数据,再以HMM模型对已提取的视频手写签名特征点与压力数据进行认证与计算,确定签名的真实度;实验仿真证明,所提算法提高了视频签名认证的性能。     

基于时间序列优化和DTW的在线手写签名鉴别

针对传统DTW算法的不足,本文通过优化时间序列,提出了基于时间序列优化的在线手写签名鉴别算法。该算法弥补了传统DTW算法忽略签名时间序列长度的不足,并提高了签名认证准确率。实验结果说明算法是可行的。     

基于特殊点的DTW在线手写签名认证算法

针对传统动态时间规正算法速度较慢的缺点,提出基于特殊点的动态时间规正在线手写签名认证算法.特殊点包含了丰富的签名信息,表现了签名者的基本生物特征.该算法缩短了身份认证的时间,提高了身份认证的效率.实验结果表明,该方法速度快、识别率高,进而证明在线手写签名在身份认证中具有很好的应用前景.     

Online signature verification using a new extreme points warping technique

There are two common methodologies to verify signatures: the functional approach and the parametric approach. In this paper, we propose a new warping technique for the functional approach in signature verification. The commonly used warping technique is dynamic time warping (DTW). It was originally used in speech recognition and has been applied in the field of signature verification with some success since two decades ago. The new warping technique we propose is named as extreme points warping (EPW). It proves to be more adaptive in the field of signature verification than DTW, given the presence of the forgeries. Instead of warping the whole signal as DTW does, EPW warps a set of selected important points. With the use of EPW, the equal error rate is improved by a factor of 1.3 and the computation time is reduced by a factor of 11.     

基于PDA的在线签名鉴别系统的设计与实现

设计并实现了一个基于PDA的在线签名鉴别系统，用于PDA的开机保护。根据PDA对于内存和运行速度的要求，选择位置和速度作为特征，改进的动态归正方法作为匹配算法，并且在Windows CE操作系统上实现了这一系统。该系统简单而高效，在错误接受率为0％的前提下，错误拒绝率仅为5％。     

On-line signature recognition based on VQ-DTW

This paper studies some pattern recognition algorithms for on-line signature recognition: vector quantization (VQ), nearest neighbor (NN), dynamic time warping (DTW) and hidden Markov models (HMM). We have used a database of 330 users which includes 25 skilled forgeries performed by five different impostors. This database is larger than the typical ones found in the literature.Experimental results reveal that our first proposed combination of VQ and DTW (by means of score fusion) outperforms the other algorithms (DTW, HMM) and achieves a minimum detection cost function (DCF) value equal to 1.37% for random forgeries and 5.42% for skilled forgeries. In addition, we present another combined DTW-VQ scheme which enables improvement of privacy for remote authentication systems, avoiding the submission of the whole original dynamical signature information (using codewords, instead of feature vectors). This system achieves similar performance than DTW.     

Model-based signature verification with rotation invariant features

Non-linear rotation of signature patterns is one of the major difficulties to solve in off-line signature verification. This paper presents two models utilizing rotation invariant structure features to tackle the problem. In principle, the elaborately extracted ring-peripheral features are able to describe internal and external structure changes of signatures periodically. In order to evaluate match score quantitatively, discrete fast fourier transform is employed to eliminate phase shift and verification is conducted based on a distance model. In addition, the ring-hidden Markov model (HMM) is constructed to directly evaluate similar between test signature and training samples. With respect to the side effect of outlier training samples for stable statistical model and threshold estimation, we propose a selection strategy to improve the performance of system. Experimental results demonstrated that the proposed methods were effective to improve verification accuracy.     

基于改进DTW算法的手写签名识别系统设计

在线手写签名验证是一种基于生物特征的身份认证技术。为提高签名验证的效率,该文介绍了一种改进的在线签名识别算法。它优化了传统的动态时间弯折算法结构,提出了对最佳匹配路径的动态规划方法并将其应用于在线签名识别系统中。在模板较多时对匹配距离将适当限制,从而减少了系统运算量,提高了模板匹配速率。随着待识别模板数目的增多,该算法的效率优势更加明显。试验结果表明,该改进算法的运算效率高,误拒率和误纳率较低。     

A Hidden Markov Model approach to online handwritten signature verification

A method for the automatic verification of online handwritten signatures using both global and local features is described. The global and local features capture various aspects of signature shape and dynamics of signature production. We demonstrate that adding a local feature based on the signature likelihood obtained from Hidden Markov Models (HMM), to the global features of a signature, significantly improves the performance of verification. The current version of the program has 2.5% equal error rate. At the 1% false rejection (FR) point, the addition of the local information to the algorithm with only global features reduced the false acceptance (FA) rate from 13% to 5%. Received June 27, 1997/ Revised October 31, 1997