{"title":"A Writer-Dependent Approach to Offline Signature Verification Based on One-Class Support Vector Machine","authors":"V. V. Starovoitov, U. Yu. Akhundjanov","doi":"10.1134/s1054661824700135","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>A new solution to the problem of offline signature verification is presented. Digital images of signatures are processed and converted into a binary representation of a certain size. Then their contours are traced, and from them, two original features are calculated for describing the local structural features of the signature in the form of vectors of normalized frequency distributions of local binary pattern codes and values of local curvature of the signature contours. A new feature space is formed in which the pattern describes the proximity of pairs of signatures, and its coordinates are the rank correlation coefficients between the feature vectors of these signatures. In real practice, the expert has <i>M</i> (from 5 to 15) genuine signatures of a person; there are no forged signatures at all. On these <i>M</i> available genuine signatures of a single person, we train a one-class support vector machine model and obtain a single-writer-dependent classifier. A verifiable signature is considered forged if the classifier model considers it to be an outlier. The accuracy of our approach in verifying the genuineness of all 2640 signatures from the CEDAR database was 99.77%. All forged signatures in this database were correctly recognized.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"23 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PATTERN RECOGNITION AND IMAGE ANALYSIS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1134/s1054661824700135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
A new solution to the problem of offline signature verification is presented. Digital images of signatures are processed and converted into a binary representation of a certain size. Then their contours are traced, and from them, two original features are calculated for describing the local structural features of the signature in the form of vectors of normalized frequency distributions of local binary pattern codes and values of local curvature of the signature contours. A new feature space is formed in which the pattern describes the proximity of pairs of signatures, and its coordinates are the rank correlation coefficients between the feature vectors of these signatures. In real practice, the expert has M (from 5 to 15) genuine signatures of a person; there are no forged signatures at all. On these M available genuine signatures of a single person, we train a one-class support vector machine model and obtain a single-writer-dependent classifier. A verifiable signature is considered forged if the classifier model considers it to be an outlier. The accuracy of our approach in verifying the genuineness of all 2640 signatures from the CEDAR database was 99.77%. All forged signatures in this database were correctly recognized.
摘要 针对离线签名验证问题提出了一种新的解决方案。签名的数字图像经过处理后转换成一定大小的二进制表示。然后对其轮廓进行追踪，并从中计算出两个原始特征，以局部二进制模式代码的归一化频率分布向量和签名轮廓的局部曲率值的形式来描述签名的局部结构特征。这样就形成了一个新的特征空间，其中的模式描述了成对签名的接近程度，其坐标则是这些签名特征向量之间的秩相关系数。在实际操作中，专家拥有一个人的 M 个（从 5 到 15 个）真实签名，没有任何伪造签名。在这 M 个可用的单人真实签名上，我们训练了一个单类支持向量机模型，并得到了一个依赖于单个作者的分类器。如果分类器模型认为一个可验证的签名是异常值，那么这个签名就被认为是伪造的。我们的方法验证 CEDAR 数据库中所有 2640 个签名真实性的准确率为 99.77%。该数据库中的所有伪造签名均被正确识别。
期刊介绍：
The purpose of the journal is to publish high-quality peer-reviewed scientific and technical materials that present the results of fundamental and applied scientific research in the field of image processing, recognition, analysis and understanding, pattern recognition, artificial intelligence, and related fields of theoretical and applied computer science and applied mathematics. The policy of the journal provides for the rapid publication of original scientific articles, analytical reviews, articles of the world''s leading scientists and specialists on the subject of the journal solicited by the editorial board, special thematic issues, proceedings of the world''s leading scientific conferences and seminars, as well as short reports containing new results of fundamental and applied research in the field of mathematical theory and methodology of image analysis, mathematical theory and methodology of image recognition, and mathematical foundations and methodology of artificial intelligence. The journal also publishes articles on the use of the apparatus and methods of the mathematical theory of image analysis and the mathematical theory of image recognition for the development of new information technologies and their supporting software and algorithmic complexes and systems for solving complex and particularly important applied problems. The main scientific areas are the mathematical theory of image analysis and the mathematical theory of pattern recognition. The journal also embraces the problems of analyzing and evaluating poorly formalized, poorly structured, incomplete, contradictory and noisy information, including artificial intelligence, bioinformatics, medical informatics, data mining, big data analysis, machine vision, data representation and modeling, data and knowledge extraction from images, machine learning, forecasting, machine graphics, databases, knowledge bases, medical and technical diagnostics, neural networks, specialized software, specialized computational architectures for information analysis and evaluation, linguistic, psychological, psychophysical, and physiological aspects of image analysis and pattern recognition, applied problems, and related problems. Articles can be submitted either in English or Russian. The English language is preferable. Pattern Recognition and Image Analysis is a hybrid journal that publishes mostly subscription articles that are free of charge for the authors, but also accepts Open Access articles with article processing charges. The journal is one of the top 10 global periodicals on image analysis and pattern recognition and is the only publication on this topic in the Russian Federation, Central and Eastern Europe.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
