Off-line signature verification by the tracking of feature and stroke positions

There are inevitable variations in the signature patterns written by the same person. The variations can occur in the shape or in the relative positions of the characteristic features. In this paper, two methods are proposed to track the variations. Given the set of training signature samples, the first method measures the positional variations of the one-dimensional projection profiles of the signature patterns; and the second method determines the variations in relative stroke positions in the two-dimension signature patterns. The statistics on these variations are determined from the training set. Given a signature to be verified, the positional displacements are determined and the authenticity is decided based on the statistics of the training samples. For the purpose of comparison, two existing methods proposed by other researchers were implemented and tested on the same database. Furthermore, two volunteers were recruited to perform the same verification task. Results show that the proposed system compares favorably with other methods and outperforms the volunteers.     

Recognition of writers by handwriting images

The objective of this paper is the automatic identification of individual persons by means of their handwriting images. The image, digitized by a computer-controlled TV-camera, is analyzed in two different ways. In terms of the writing process, it is first interpreted as a set of lines, which have to be reconstructed by methods of scene analysis.

Secondly, a special non-linear transformation is applied to the image, which provides features representing global statistical information about the image. In a classification experiment involving 20 writers (40 samples of each), 797 out of 800 samples were identified correctly.     

基于几何中心静态手写签名的识别算法研究*

研究了静态手写体签名识别和认证的问题。针对静态手写体签名无法提供笔画之间前后时序动态信息和手写笔画的压力信息,提出了一种利用手写签名的几何中心作为特征值的识别和认证算法。首先将静态签名图像依据几何中心不断进行切分,使其成为独立的小块;然后依据各个小块的几何中心的相对位置和距离提取特征值;在此基础上进行签名识别和认证。实验结果显示本方法快速有效,所提取的特征能稳定地描述包含集合形变的手写签名字体。该方法能拓展应用到手写体的识别系统中。     

基于小波变换和SVM的眉毛身份验证系统研究*

为了对眉毛这一新颖生物特征开展识别研究,提出了一种基于小波变换方法和支持向量机（SVM）的眉毛身份验证方法。其基本思想是用小波变换提取眉毛图像特征,然后用SVM进行训练和验证。在自建的100人眉毛数据库中进行的实验结果表明,该系统具有较低的错误拒绝率29.58%和错误接受率8.22%,从而验证了眉毛用于个人身份鉴别的可能性和有效性。     

基于多分类器组合的笔迹验证

运用多分类器组合技术和模糊技术将多种笔迹鉴别方法按一定规则进行融合，针对笔迹鉴别中的笔迹验证问题进行应用。实验结果表明，融合后笔迹验证准确率有大幅的提高     

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。     

Palmprint Recognition by Applying Wavelet-Based Kernel PCA

This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coeffcients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coeffcients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classifier. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.     

离线签名验证中的多分类器集成

在离线签名验证的分类器设计中,为了减少特征向量分布不均和维数过高对实验结果的影响,给出一种多分类器集成的方法.根据特征向量数量级的不同进行分组,各组分类器自适应地确定分类器权重,通过投票表决得出集成判决结果.实验结果表明,通过分组和加权后,分类正确率有明显提高.     

Off-line signature verification based on grey level information using texture features

A method for conducting off-line handwritten signature verification is described. It works at the global image level and measures the grey level variations in the image using statistical texture features. The co-occurrence matrix and local binary pattern are analysed and used as features. This method begins with a proposed background removal. A histogram is also processed to reduce the influence of different writing ink pens used by signers. Genuine samples and random forgeries have been used to train an SVM model and random and skilled forgeries have been used for testing it. Results are reasonable according to the state-of-the-art and approaches that use the same two databases: MCYT-75 and GPDS-100 Corpuses. The combination of the proposed features and those proposed by other authors, based on geometric information, also promises improvements in performance.     

Dealing with a priori knowledge by fuzzy labels

The performances of two different estimators of a discriminant function of a statistical pattern recognizer are compared. One estimator is based on binary label values of the objects of the learning set (hard labels) and the other on continuous or multi-discrete label values in the interval [0,1] (fuzzy labels). By the latter estimator more detailed a priori knowledge of the contributing learning objects is used. In a discrete feature space, in which a multi-nomial distribution function has been assumed to exist, the expected classification error, based on fuzzy labels, can be more accurate than the one based on hard