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Open AccessArticle

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

by Khaled Daqrouq 1,*,†, Husam Sweidan 2,†, Ahmad Balamesh 1,† and Mohammed N. Ajour 1,†
1
Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2
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
Entropy 2017, 19(6), 252; https://doi.org/10.3390/e19060252
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
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Daqrouq, K.; Sweidan, H.; Balamesh, A.; Ajour, M.N. Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network. Entropy 2017, 19, 252.

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