Title :
Online signature verification using neural network and pearson correlation features
Author :
Iranmanesh, Vahab ; Mumtazah Syed Ahmad, Sharifah ; Wan Adnan, Wan Adilah ; Layth Malallah, Fahad ; Yussof, Salman
Author_Institution :
Fac. of Eng., Univ. of Putra (UPM), Serdang, Malaysia
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%.
Keywords :
backpropagation; correlation methods; database theory; feature extraction; handwriting recognition; neural nets; Pearson correlation features; SIGMA database; backpropagation neural network; feature extraction; online signature verification; pen pressure; signature coordinate points; Accuracy; Biological system modeling; Correlation; Feature extraction; Protocols; Feature Extraction; Neural Network; Online Signature Verification; Pattern Recognition; Pearson Correlation Coefficients;
Conference_Titel :
Open Systems (ICOS), 2013 IEEE Conference on
Conference_Location :
Kuching
Print_ISBN :
978-1-4799-3152-1
DOI :
10.1109/ICOS.2013.6735040
