@InProceedings{PachasGast:2021:OfWrSi,
               author = "Pachas, Felix Eduardo Huaroto and Gastal, Eduardo S. L.",
          affiliation = "{Instituto de Inform{\'a}tica -- UFRGS } and {Instituto de 
                         Inform{\'a}tica -- UFRGS}",
                title = "An Offline Writer-Independent Signature Verification Method with 
                         Robustness Against Scalings and Rotations",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "offline, writer-independent, signature verification, convolutional 
                         neural network, CLIP.",
             abstract = "Handwritten signatures are still one of the most used and accepted 
                         methods for user identification and authentication. They are used 
                         in a wide range of human daily tasks, including applications from 
                         banking to legal processes. The signature verification problem 
                         consists of verifying whether a given handwritten signature was 
                         generated by a particular person, by comparing it (directly or 
                         indirectly) to genuine signatures from that person. In this paper, 
                         we introduce a new offline writer-independent signature 
                         verification method based on a combination of handcrafted Moving 
                         Least-Squares features and features transferred from a 
                         convolutional neural network. In our experiments, our method 
                         outperforms state-of-the-art techniques on Western-style 
                         signatures (CEDAR dataset), while also obtaining good results on 
                         South Asian-style handwriting (Bangla and Hindi datasets). 
                         Furthermore, we demonstrate that the proposed method is the most 
                         robust in relation to differences in scale and rotation of the 
                         signature images. We also present a discussion on dataset bias and 
                         a small user study, showing that our technique outperforms the 
                         expected human accuracy on the signature-verification task.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00051",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00051",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45AQRAS",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45AQRAS",
           targetfile = "Huaroto_Gastal_SIBGRAPI_2021_Signature_Verification.pdf",
        urlaccessdate = "2024, May 06"
}