Next Article in Journal
Multiscale Entropy Analysis of the Differential RR Interval Time Series Signal and Its Application in Detecting Congestive Heart Failure
Next Article in Special Issue
A Novel Distance Metric: Generalized Relative Entropy
Previous Article in Journal
Horton Ratios Link Self-Similarity with Maximum Entropy of Eco-Geomorphological Properties in Stream Networks
Previous Article in Special Issue
Classification of Fractal Signals Using Two-Parameter Non-Extensive Wavelet Entropy

Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network

by 1,*,†, 2,†, 1,† and 1,†
Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI 49931, USA
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Carlo Cattani
Received: 7 March 2017 / Revised: 7 May 2017 / Accepted: 10 May 2017 / Published: 31 May 2017
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory II)
Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%. View Full-Text
Keywords: wavelet; entropy; signature; threshold entropy; PNN wavelet; entropy; signature; threshold entropy; PNN
Show Figures

Figure 1

MDPI and ACS Style

Daqrouq, K.; Sweidan, H.; Balamesh, A.; Ajour, M.N. Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network. Entropy 2017, 19, 252.

AMA Style

Daqrouq K, Sweidan H, Balamesh A, Ajour MN. Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network. Entropy. 2017; 19(6):252.

Chicago/Turabian Style

Daqrouq, Khaled, Husam Sweidan, Ahmad Balamesh, and Mohammed N. Ajour. 2017. "Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network" Entropy 19, no. 6: 252.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop