Honors Theses

Date of Award

Spring 5-9-2020

Document Type

Undergraduate Thesis


Computer and Information Science

First Advisor

Dawn Wilkins

Second Advisor

Yixin Chen

Third Advisor

James Adam Jones

Relational Format



Statistical and machine learning approaches to forgery detection in offline sig- natures are attempted and evaluated. Offline signatures are static signatures found on physical media, mainly a piece of paper. A dataset of 330 signatures for 33 people is used, containing five genuine and five forged signatures for each person. The statistical analysis approach proves more successful than a machine learning approach, likely due to the size of the dataset.

Accessibility Status

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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.



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