Authors: Ali Salem Altaher; Saleem Mohammed Ridha Taha
Addresses: Department of Electrical Engineering, College of Engineering, University of Baghdad, Jadiryah, Baghdad, Iraq ' Department of Electrical Engineering, College of Engineering, University of Baghdad, Jadiryah, Baghdad, Iraq
Abstract: The finger knuckle print (FKP) images are used for personal authentication. The proposed model consists of pre-processing of the FKP image and then feature extraction algorithm is applied to extract coefficients that will be used in the matching process. In the classification process, improved versions of neural networks [quantum neural network (QNN), wavelet neural network (WNN) and quantum wavelet neural network (QWNN)] are used to approach better accuracy and speed of convergence. This paper has precedence in implementation of the quantum computing (QC) in the structure of the FKP recognition system. It has advantages of low inexactness and high speed of execution by using the quantum superposition state ideology. A database gathered from 165 volunteers by Hong Kong Polytechnic University (Poly U) and the proposed authentication model performance is tested upon it. Compared with other existing FKP recognition systems, the proposed one has merits of more secure as well as high accuracy and speed.
Keywords: finger knuckle print; FKP; personal authentication; quantum computing; quantum neural network; QNN; quantum wavelet neural network; QWNN; wavelet neural network; WNN.
International Journal of Biometrics, 2017 Vol.9 No.2, pp.129 - 142
Received: 13 Sep 2016
Accepted: 21 Apr 2017
Published online: 05 Aug 2017 *