Handwritten signature verification using shallow convolutional neural network

Multimedia Tools and Applications - Handwritten signatures are an undeniable and unique way to prove the identity of persons. Owing to the simplicity and uniqueness, it finds an essential place in...     

Biometric signature verification system based on freeman chain code and k-nearest neighbor

Signature is one of human biometrics that may change due to some factors, for example age, mood and environment, which means two signatures from a person cannot perfectly matching each other. A Signature Verification System (SVS) is a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. Before extracting the features, the raw images will undergo preprocessing stage; binarization, noise removal, cropping and thinning. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated against split image to test the feature efficiency. Finally, verification utilized Euclidean distance to measured and matched in k-Nearest Neighbors. MCYT bimodal database was used in every stage in the system. Based on the experimental results, the lowest error rate for FRR and FAR were 6.67 % and 12.44 % with AER 9.85 % which is better in term of performance compared to other works using that same database.     

Online signature verification based on signatures turning angle representation using longest common subsequence matching

Online signature verification has been intensively investigated in several directions, such as the selected feature(s), similarity estimation and classification method. Local feature approaches combined with elastic distance metrics have the most successful performance so far. The Turning Angle Sequence (TAS) feature has not been extensively explored for signature verification, while the fusion of TASs of different scales, the Turning Angle Scale Space (TASS) is a new approach in this field. In this paper, we study the signatures TAS and TASS representations and their application to online signature verification. In the matching stage, a variation of the longest common sub-sequence matching technique has been employed. Experimental results using varying TAS(S) representation parameters on two publicly available signature databases, the SVC2004 and SUSIG, show the improved performance of the selected feature along with the chosen elastic distance measure on the equal error rate results of the online signature verification task.     

On-line signature verification based on spatio-temporal correlation

We address the problem of on-line signature representation and verification. A novel strategy is proposed in this paper by combining texture based image analysis and spatio-temporal representation. Firstly, a correlation based method is proposed to describe spatio-temporal information between sampling points, which is then converted to traditional 2D intensity images. Secondly, abundant texture analysis methods are adopted to construct effective features for high accuracy verification. Furthermore, a template selection strategy based on intra-class variations is presented to further enhance the performance of signature verification. Extensive experiments are conducted on the SVC2004 database and experimental results demonstrate the inspiring performance of our proposed methods.     

Online signature verification based on string edit distance

International Journal on Document Analysis and Recognition (IJDAR) - Handwritten signatures are widely used and well-accepted biometrics for personal authentication. The accuracy of signature...     

Palmprint verification based on robust line orientation code

In this paper, we propose a novel robust line orientation code for palmprint verification, whose performance is improved by using three strategies. Firstly, a modified finite Radon transform (MFRAT) is proposed, which can extract the orientation feature of palmprint more accurately and solve the problem of sub-sampling better. Secondly, we construct an enlarged training set to solve the problem of large rotations caused by imperfect preprocessing. Finally, a matching algorithm based on pixel-to-area comparison has been designed, which has better fault tolerant ability. The experimental results of verification on Hong Kong Polytechnic University Palmprint Database show that the proposed approach has higher recognition rate and faster processing speed.     

基于二维条码的身份验证系统

本文以PDF417二维条码作为载体,将作为身份标识的数字、汉字、指纹等信息处理后进行编码,形成二维条码文件,打印在普通纸的身份卡证件上或其它电子证件上作为身份验证的模板;通过便携式解码装置可获得条码的具体内容,应用模糊模式识别方法与现场实时采集的指纹信息进行快速在线的身份验证,形成一个识别率高、成本低、可移动式的实用验证系统,误判率大大降低.     

Interval valued symbolic representation of writer dependent features for online signature verification

This work focusses on exploitation of the notion of writer dependent parameters for online signature verification. Writer dependent parameters namely features, decision threshold and feature dimension have been well exploited for effective verification. For each writer, a subset of the original set of features are selected using different filter based feature selection criteria. This is in contrast to writer independent approaches which work on a common set of features for all writers. Once features for each writer are selected, they are represented in the form of an interval valued symbolic feature vector. Number of features and the decision threshold to be used for each writer during verification are decided based on the equal error rate (EER) estimated with only the signatures considered for training the system. To demonstrate the effectiveness of the proposed approach, extensive experiments are conducted on both MCYT (DB1) and MCYT (DB2) benchmarking online signature datasets consisting of signatures of 100 and 330 individuals respectively using the available 100 global parametric features.     

On-line signature verification

We describe a method for on-line handwritten signature verification. The signatures are acquired using a digitizing tablet which captures both dynamic and spatial information of the writing. After preprocessing the signature, several features are extracted. The authenticity of a writer is determined by comparing an input signature to a stored reference set (template) consisting of three signatures. The similarity between an input signature and the reference set is computed using string matching and the similarity value is compared to a threshold. Several approaches for obtaining the optimal threshold value from the reference set are investigated. The best result yields a false reject rate of 2.8% and a false accept rate of 1.6%. Experiments on a database containing a total of 1232 signatures of 102 individuals show that writer-dependent thresholds yield better results than using a common threshold.     

改进的基于椭圆曲线的签名验证快速算法

通过对文献中提出的基于Montgomery思想的签名验证快速算法进行分析,发现其有一定的局限性,只适用于两个标量的二进制长度相等且最高首位比特为1的情况,通过对其算法进行改进,在不增加运算量的情况下,使其对任意标量都适用,这对椭圆曲线多标量乘及签名验证快速实现具有实际意义.     

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.     

New method for the on-line signature verification based on horizontal partitioning

Verification of identity based on the analysis of dynamic signatures is an important problem of biometrics. The effectiveness of the verification significantly increases when the dynamic characteristics of signature (e.g. velocity of the pen) are considered. These characteristics are individual for each user and difficult to imitate. The effectiveness of the verification can be further improved by using partitioning. In this paper we propose a new method which uses partitioning. In our method partitions represent areas of high and low speed of signature and high and low pen?s pressure. All selected partitions are used by our algorithm, but more important in the classification process are these partitions, in which the signatures of the user acquired during training phase are more stable. Moreover, final classification is interpretable. In this paper we present the simulation results of the proposed method for the two databases: SVC2004 and BioSecure Database.     

On-line signature verification using vertical signature partitioning

In this paper we propose a new approach to identity verification based on the analysis of the dynamic signature. Considered problem seems to be particularly important in terms of biometrics. Effectiveness of signature verification significantly increases when dynamic characteristics of the signature are considered (e.g. velocity, pen pressure, etc.). These characteristics are individual for each user and difficult to forge. The effectiveness of the verification on the basis of an analysis of the dynamics of the signature can be further improved. A well-known way is to consider the characteristics of the signature in the sections called partitions. In this paper we propose a new method for identity verification which uses partitioning. Partitions represent time moments of signing of the user. In the classification process the partitions, in which the user created more stable reference signatures during acquisition phase, are more important. Other important features of our method are: using capabilities of fuzzy set theory and development on the basis of them the flexible neuro-fuzzy systems and interpretable classification system for final signature classification. In this paper we have included the simulation results for the two currently available databases of dynamic signatures: free SVC2004 and commercial BioSecure database.     

基于在线手写签名的身份认证技术研究和展望

在线签名鉴定是身份认证技术中的一种有效方法。本文简要回顾了基于在线手写签名的身份认证技术的研究背景及发展历程;重点对近年签名鉴定技术的研究进展进行综述并对各种方法予以评价;总结了现存的研究困难并分析了应用前景和发展方向。     

基于小波包分析的在线手写签名认证方法

针对在线手写签名难以提取有效特征的实际情况,提出用小波包分解和单支重构来构造能量特征向量的方法,直接利用各频段成分能量的变化来反映签名的动态特征。用该方法构造的特征向量能突出反映签名的动态特征,通过RBF神经网络进行签名识别。实验数据表明,采用此方法,识别的正确率可达96.75%,平均错误率ERR=3.34%,其性能是较满意的。     

Online signature verification based on null component analysis and principal component analysis

This paper describes a method for stroke-based online signature verification using null component analysis (NCA) and principal component analysis (PCA). After the segmentation and flexible matching of the signature, we extract stable segments from each reference signature in order that the segment sequences have the same length. The reference set of feature vectors are transformed and separated into null components (NCs) and principal components (PCs) by K-L transform. Online signature verification is a special two-category classification problem and there is not a single available forgery set in an actual system. Therefore, it is different from the typical application of PCA in pattern recognition that both NCA and PCA are used to respectively analyze stable and unstable components of genuine reference set. Experiments on a data set containing a total 1,410 signatures of 94 signers show that the NCA/PCA-based online signature verification method can achieve better results. The best result yields an equal error rate of 1.9%.     

A novel biometric system for signature verification based on score level fusion approach

Multimedia Tools and Applications - The active modality of handwriting is broadly related to signature verification in the context of biometric user authentication systems. Signature verification...     

A deep feature warehouse and iterative MRMR based handwritten signature verification method

Multimedia Tools and Applications - Offline handwritten signature verification has been widely used for document forensics and biometrics, and it is a popular issue. Deep learning models have...