Bibliographic citations
Huaroto, F., (2022). An Offline Writer-Independent Signature Verification Method with Robustness Against Scalings and Rotations [Tesis, Universidade Federal do Rio Grande do Sul]. https://renati.sunedu.gob.pe/handle/sunedu/3434524http://hdl.handle.net/10183/247543
Huaroto, F., An Offline Writer-Independent Signature Verification Method with Robustness Against Scalings and Rotations [Tesis]. BR: Universidade Federal do Rio Grande do Sul; 2022. https://renati.sunedu.gob.pe/handle/sunedu/3434524http://hdl.handle.net/10183/247543
@mastersthesis{renati/6885,
title = "An Offline Writer-Independent Signature Verification Method with Robustness Against Scalings and Rotations",
author = "Huaroto Pachas, Felix Eduardo",
publisher = "Universidade Federal do Rio Grande do Sul",
year = "2022"
}
Handwritten signatures are still one of the most used and accepted methods for user 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 research work, a new offline writer-independent signature verification method is introduced (named VerSig-R), based on a combination of handcrafted Moving Least-Squares features and features transferred from a convolutional neural network. In our experiments, VerSig-R outperforms state-of-the-art techniques on Western-style signatures (CEDAR dataset), while also obtaining competitive results on South Asian-style handwriting (Bangla and Hindi datasets). Furthermore, a wide range of experiments demonstrate that VerSig-R is the most robust in relation to differences in scale and rotation of the signature images. This work also presents a discussion on dataset bias and on cross-dataset performance of VerSig-R, as well as a small user study showing that the proposed technique outperforms the expected human accuracy on the signature-verification task. Finally, a discussion on the impact of the number of signature examples (per writer) used during training on performance and execution time is presented.
File | Description | Size | Format | |
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HuarotoPachasFE.pdf Restricted Access | Tesis (abierta en repositorio de origen) | 1.58 MB | Adobe PDF | View/Open Request a copy |
Autorizacion.pdf Restricted Access | Autorización del registro | 564.99 kB | Adobe PDF | View/Open Request a copy |
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