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International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8References[1] K. Fazel and S. Kaiser, Multi-Carrier and Spread Spectrum Systems: From OFDM and MC-CDMA to LTEand WiMAX, 2nd Edition, John Wiley & Sons, 2008, ISBN 978-0-470-99821-2[2]. M. Ergen, Mobile Broadband - Including WiMAX and LTE, Springer, NY, 2009 ISBN 978-0-387-68189-4[3]. Carl Weinschenk (April 16, 2010). "Speeding Up WiMax". IT Business Edge. "Today the initial WiMaxsystem is designed to provide 30 to 40 megabit-per-second data rates."[4]. Wimax Forum Industry Research Reporthttp://www.wimaxforum.org/sites/wimaxforum.org/files/page/2011/03/Monthly_Industry_Report_March2011.pdf[5] Synthesis of band-limited orthogonal signals for multi-channel data transmission, Bell System TechnicalJournal 46, 1775-1796.[6] MATLAB 7.8.0 (R2009a), Help. s.l. : MathWorks, Inc., 2009.[7] van Nee, R. and Prasad, R. OFDM for Wireless Multimedia Communications. s.l. : Artech House, 2000.[8] Charan Langton. OFDM. Intuitive Guide to Principles of Communications. http://www.complextoreal.com/.[9] Amalia Roca Persiva . Implementation of a WiMAX simulator in Simulink. Institute of Communications &Radio-Frequency Engineering, Vienna University of Technology. Vienna : s.n., 2007. Master Thesis.Issn 2250-3005(online) December| 2012 Page 81
International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8Study and comparison of various point based feature extraction methodsin palmprint authentication systemVinod Kumar D 1 , Dr. Nagappan A 21Department of ECE & CSE,2Principal, V.M.K.V. Engineering College,Vinayaka Missions Research Foundation Deemed UniversitySalem, Tamilnadu, IndiaAbstractBiometrics is the word derived from Greek words ―Bio‖ means (life) & ―Metrics‖ means (to measure). Biometrics isthe science of measuring human characteristics for the purpose of authenticating or identifying the identity of an individual.Biometrics System is used for automated recognition of an individual. In Information system in particular biometrics is usedas a form of identity access management and access control. It is also used to identify individuals in groups that are undersurveillance. In this paper, palmprint biometric is used for personal authentication. Various feature extraction methods to bediscussed and compared are Forstner operator, SUSAN operator, Wavelet based salient point detection and Tra jkovic andHedley corner detector.Keywords: Biometric, Corner, Palmprint, Palmprint features.1. IntroductionThe palmprint of a person can be also taken as a biometric. It is a physiological biometrics, palmprint, the inner partof a person‘s hand, below the fingers to the wrist meets out both the theoretical and practical requirements to be a biometric.Palmprint is universal because every person has palmprint. It is unique because every palmprint is different from other personpalmprint even identical twins have different palmprint features. Palmprint is permanent or inseparable from individual ascompared to identification items. It is easy to collect and consistent because it does not change much with time. It performsbetter in term of accuracy, speed and robustness. Palmprint biometric system is more acceptable by public because users cangain access anytime they want without being monitored by a surveillance camera. It is hard to imitate because of its size.Palmprint is features rich. It consists of geometry features, point features, line features, texture features and statisticalfeatures. Palmprint point features are datum points, line intersection points, end line points etc. Point features can be obtainedthrough high resolution palmprint image. In this paper, several point (corner) based feature extraction methods are studied andcompared. Results of various feature extraction methods are compared and among them best method is found out.2. Corner based feature extraction methodsCorners are the points where intensity changes in all directions. In palmprint, palmprint features can be of the form ofcorner points. These features can be extracted using Forstner operator, SUSAN operator etc. These meth ods are discussed indetail as follows:2.1 Förstner OperatorThe Förstner Operator developed by Förstner and Gülch in 1987 has been widely adopted in photogrammetry andcomputer vision over the last two decades. The aim of developing this operator is to create a fast operator for the detection andprecise location of distinct points, corners and centres of circular image features. The algorithm identifies interest points, edgesand regions using the autocorrelation function A. The derivatives of A are computed and summed over a Gaussian window.Error ellipses are computed and based on the size and shape properties of each ellipse, the interest points found are classifiedas points, edges or regions. Förstner calculates the size and shape of the error ellipses using two eigenvalues λ1 and λ2 as wellas the inversion of A.The error ellipse size is determined by:w1 det trace12AA,w 0Issn 2250-3005(online) December| 2012 Page 82
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