Improving Signature Forgery Analysis through Deep Learning Classification Algorithms

Authors

  • Kashvi Gupta Tesla STEM High School
  • Shashank Gupta

DOI:

https://doi.org/10.47611/jsrhs.v9i2.1229

Keywords:

signature forgery, classification algorithms, machine learning, handwriting analysis, deep learning

Abstract

This project seeks to develop a machine learning algorithm to identify a forgery from a legitimate signature, for use in signature verification in low profile financial crimes. This model will be trained on data collected on specific handwriting characteristics used by professional document analysis experts. Signature forgery in financial institutions was recently brought to light in the Wells Fargo fake accounts scandal, where employees opened 3.5 million unauthorized accounts, for which 190,000 customers were unwittingly charged fees. This product will help protect individuals from exploitation by providing a verification tool for company managers and executives. The hypothesis predicted if characteristics such as length-to-height ratio and relative slant were taken into account, then the accuracy of the model would be greater than 90%. Each data point consisted of 6 signatures, of which 5 were ‘true’ (produced by the same person) and the final was forged. The data was fed to a processing program that used mathematical formulae (standard deviation from the mean) to account for, and negate human error. Finally, using the python Scikit machine learning library, multiple models were trained on the data sample, using k-fold analysis. The most successful model, XGBoost Classifier, had an accuracy rate of 94.55%.

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Published

11-20-2020

How to Cite

Gupta, K., & Gupta, S. (2020). Improving Signature Forgery Analysis through Deep Learning Classification Algorithms. Journal of Student Research, 9(2). https://doi.org/10.47611/jsrhs.v9i2.1229

Issue

Section

HS Research Articles