Online signature verification using neural network and pearson correlation features

Abstract

In this paper, we proposed a method for feature extraction in online signature verification. We first used signature coordinate points and pen pressure of all signatures, which are available in the SIGMA database. Then, Pearson correlation coefficients were selected for feature extraction. The obtained features were used in back-propagation neural network for verification. The results indicate an accuracy of 82.42%.

Neural Network Based Intelligent Retrieval System for Verifying Dynamic Signatures

“…Results are verified by using back propagation neural network and the accuracy of 82.42% is obtained. Experimental results indicate the number of false acceptance is more than false rejection [10].…”

Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

“…To improve the accuracy of signature verification, some studies [8][9][10] utilize machine learning techniques, which are one of the most noteworthy technologies. Similar to the process in feature-based signature verification, they use descriptive features of signatures for building a subject model.…”

“…Similar to the process in feature-based signature verification, they use descriptive features of signatures for building a subject model. Iranmanesh et al [9] exploited MLP for signature verification, and demonstrated an average accuracy of 82.42% for recognizing the subject; however, this method could not distinguish between imitated and original signatures. As mentioned previously, M. Fayyza et al [10] used AE, the one-class model, to distinguish imitated signatures.…”

“…A. Buriro [3] applies deep learning algorithm for finger drawn signature verification using dynamic features such as finger movements and phone movements. However, they exploit MLP, two-class classifier similar to Iranmanesh et al [9], and suffer from the same problem, i.e., trouble of distinguishing imitated signatures.…”

“…Early studies [4][5][6][7][8][9][10] primarily focused on verifying signatures drawn by a stylus. Finger-drawn signature verification [3,[11][12][13] is relatively not well researched yet; however, it is currently actively discussed because most smartphones do not have a stylus, and finger signing is more convenient.…”