Methods to Detect Forgeries in Static Signatures

Author

Jennifer LaurielloFollow

Date of Award

Spring 5-9-2020

Document Type

Undergraduate Thesis

Department

Computer and Information Science

First Advisor

Dawn Wilkins

Second Advisor

Yixin Chen

Third Advisor

James Adam Jones

Relational Format

Dissertation/Thesis

Abstract

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.

Recommended Citation

Lauriello, Jennifer, "Methods to Detect Forgeries in Static Signatures" (2020). Honors Theses. 1318.
https://egrove.olemiss.edu/hon_thesis/1318

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Creative Commons License

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