Arab (2016) Multi-spectral palmprint recognition based on oriented multiscale logGabor filters. Neurocomputing, Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) Multi-spectral palmprint Recognition based onOriented Multiscale log-Gabor Filters AbstractAmong several palmprint recognition methods proposed recently, coding-based approaches using multi-spectral palmprint images are attractive owing to their high recognition rates. Aiming to further improve the performance of these approaches, this paper presents a novel multi-spectral palmprint recognition approach based on oriented multiscale log-Gabor filters. The proposed method aims to enhance the recognition performances by proposing novel solutions at three stages of the recognition process. Inspired by the bitwise competitive coding, the feature extraction employs a multi-resolution log-Gabor filtering where the final feature map is composed by the winning codes of the lowest filters' bank response. The matching process employs a bitwise Hamming distance and Kullback-Leibler divergence as novel metrics to enable an efficient capture of the intra-and inter-similarities between palmprint feature maps. Finally, the decision stage is carried pout using a fusion of the scores generated from of different spectral bands to reduce overlapping. In addition, a fusion of the feature maps through two proposed novel feature fusion techniques to allow us to eliminate the inherent redundancy of the features of neighboring spectral bands is also * Corresponding author Email addresses: md.alioua@univ-jijel.dz, maya@ai.univ-paris8.fr (Meriem Dorsaf Bounneche), larbi.boubchir@ai.univ-paris8.fr (Larbi Boubchir), ahmed.bouridane@northumbria.ac.uk (Ahmed Bouridane), nek_cem@univ-jijel.dz (Bachir Nekhoul), aa@ai.univ-paris8.fr (Arab Ali-Chérif) Preprint submitted to Journal of L A T E X TemplatesMay 2, 2016proposed. The experimental results obtained using the multi-spectral palmprint database MS-PolyU have shown ...

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SUMMARYThis paper presents an adaptive force/position controller for a parallel robot executing constrained motions. This controller, based on an MLPNN (or Multi-Layer Perceptron Neural Network), does not require the inverse dynamic model of the robot to derive the control law. A neural identification of the dynamic model of the robot is proposed to determine the principal components of the MLPNN input vector. The latter is used to compensate the dynamic effects arising from the robot–environment interaction and its parameters are adjusted according to an adaptation law based on the Lyapunov-analysis methodology. The proposed controller is evaluated experimentally on the C5 parallel robot. This method is capable of tracking accurately the force/position trajectories and its stability robustness is proved.