[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-160304-en":3,"doc-seo-160304-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},160304,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Offline Signature Verification Using Online Handwriting Registration","This paper proposes a novel framework for offline signature verification by using online handwriting rather than handwritten images during registration. Online registrations enable robust recovery of the writing trajectory from an input offline signature, supporting effective shape matching between registration and verification signatures. New techniques are introduced: trajectory recovery formulated and solved with conditional random fields, an online context shape descriptor for alignment, and a verification criterion combining duration and amplitude variances. Experiments on a benchmark database show strong gains over established offline methods and comparable performance to online verification.","Offline Signature Verification Using Online Handwriting Registration  \nYu Qiao, Jianzhuang Liu Department of Information Engineering The Chinese University of Hong Kong  \n[qiao@gavo.t.u-tokyo.ac.jp](qiao@gavo.t.u-tokyo.ac.jp) , [jzliu@ie.cuhk.edu.hk](jzliu@ie.cuhk.edu.hk)  \nXiaoou Tang Microsoft Research Asia Beijing, China  \n[xitang@microsoft.com](xitang@microsoft.com)  \nAbstract  \nThis paper proposes a novel framework for offline signature verification. Different from previous methods, our approach makes use of online handwriting instead of handwritten images for registration. The online registrations enable robust recovery of the writing trajectory from an input offline signature and thus allow effective shape matching between registration and verification signatures. In addition, we propose several new techniques to improve the performance of the new signature verification system: 1 . we formulate and solve the recovery of writing trajectory within the framework of Conditional Random Fields; 2 . we propose a new shape descriptor, online context, for aligning signatures; 3. we develop a verification criterion which combines the duration and amplitude variances of handwriting. Experiments on a benchmark database show that the proposed method significantly outperforms the wellknown offline signature verification methods and achieve comparable performance with online signature verification methods.  \n1. Introduction  \nSignature is a socially accepted authentication method and is widely used as proof of identity in our daily life. Automatic signature verification by computers has received extensive research interests in the field of pattern recognition. Depending on the format of input information, automatic signature verification can be classified into two categories: online signature verification [6, 13, 8, 12, 5] and offline signature verification [7, 23, 15] . In the former case, a hand pad together with an instructed pen [6, 13, 8, 5] or a video camera [12] is used to obtain the online information of pen tip (position, speed, and pressure) . Therefore the input is a sequence of features. In the latter case, the input is a two-dimensional signature image captured by a scanner or other imaging device. Online signature verification has been shown to achieve much higher verification rate than offline verification [6, 13, 8, 12, 5, 7, 23, 15] . The state of the  \nart of online verification achieves equal error rates (EERs) ranging from 2% to 5%[6, 13, 8, 12, 5], while the EERs of offline verification are still as high as 10%-30%[12, 7, 23] . This difference is largely due to the availability of dynamic information in online system [5, 15] . Roughly speaking, the matching and annotation problems for 2D images are moredifficult and time consuming than those for 1D sequences. Although online verification outperforms the offline one, its use of special devices for recording the pen-tip trajectory increases its system cost and brings constraints on its applications. In some situations, such as check transaction and document verification, offline signature is obligatory. This paper focuses on offline signature verification, and our objective is to discriminate between genuine signatures and skilled forgeries which are written by careful imitation.  \nVarious features have been proposed for signature verification tasks. These features can be roughly divided into two types [13, 5]: 1) global features which are extracted from the whole signature, including block codes [7, 23], Wavelet and Fourier series [13], etc. ; 2) local features which are calculated to describe the geometrical and topological characteristics of local segments, such as position, tangent direction, and curvature [13, 8, 5, 12] . The global features can be extracted easily and are robust to noise. But they only deliver limited information for signature verification [13, 8] . On the other hand, local features provide rich descriptions of writing shapes and are powerful for discriminating writers, but the","cbCaiuhwkH1VUpbX","https://ap.wps.com/l/cbCaiuhwkH1VUpbX","pdf",301761,1,"English","en",105,"# Introduction\n## Offline vs. online signature verification\n## Features for signature verification\n## Recovering writing trajectories from offline images","[{\"question\":\"What is the core idea of the proposed offline signature verification framework?\",\"answer\":\"It uses online handwriting during registration to recover the writing trajectory from an offline signature image, then performs verification by matching signatures based on the recovered trajectory.\"},{\"question\":\"How is the writing trajectory recovery formulated and solved?\",\"answer\":\"Trajectory recovery is formulated and solved within a conditional random fields framework.\"},{\"question\":\"What new techniques improve verification performance?\",\"answer\":\"The paper introduces an online context shape descriptor for signature alignment and a verification criterion that combines duration and amplitude variances of handwriting.\"}]","Offline Signature Verification Using Online Handwriting Registration | 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is the core idea of the proposed offline signature verification framework?","Question",{"text":75,"@type":76},"It uses online handwriting during registration to recover the writing trajectory from an offline signature image, then performs verification by matching signatures based on the recovered trajectory.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the writing trajectory recovery formulated and solved?",{"text":80,"@type":76},"Trajectory recovery is formulated and solved within a conditional random fields framework.",{"name":82,"@type":73,"acceptedAnswer":83},"What new techniques improve verification performance?",{"text":84,"@type":76},"The paper introduces an online context shape descriptor for signature alignment and a verification criterion that combines duration and amplitude variances of 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