Paper Key : IRJ************327
Author: Mr. T. Arivanantham,Pratiksha S.,Zainab S.,Kiran P. ,Shubham S.
Date Published: 09 Nov 2024
Abstract
In this review, we present an offline signature forgery detection system utilizing Convolutional Neural Networks (CNN) and Principal Component Analysis (PCA). Handwritten signatures are often forged for fraudulent purposes, necessitating robust detection methods. Our system aims to classify signatures as genuine or forged by extracting key features using CNN, which captures the intricate details of the signature, such as strokes and angles. PCA is applied to reduce the dimensionality of the feature set, ensuring efficient computation without losing critical information. This hybrid approach leverages CNN for its strength in feature extraction and PCA for enhancing the discriminative power of those features. Our model is trained and tested on public datasets, demonstrating significant accuracy improvements over traditional methods, achieving up to 99.7% recognition accuracy. By applying fixed parameter thresholding, our system effectively detects both genuine and random forgeries, minimizing false positives and negatives. This research lays the groundwork for further improvements in forgery detection, proposing a scalable solution for real-world applications.
DOI Requested