A Blended Biometric Approach Using Matching Score Level Architecture
DOI:
https://doi.org/10.26438/ijcse/v6i12.777780Keywords:
Biometric Identity, IRIS Recognition, Finger print, Face Recognition, DWT, WAMSAbstract
This paper aims at security authentication for an unmanned surveillance system. The system takes the Face image, impressions of a person’s finger and images of eyes and prepares a database. A blended biometric approach is followed for calculating the weighted average of scores appraised from the three most trivial biometric traits, Face, Eye and Finger impressions. The features are extracted from the pre-processed images of iris, face and finger impressions.The details of a probing image are to be matched with the database we have .the individual details obtained after tallying are sent to the fusion module. This module consists of three major steps i.e., Pre-Processing, Discrete Wavelet Transformation and Image fusion. At the final phase the hidden key Analysis approach is followed to authenticate the subject under investigation.
References
[1] Hiren D. Joshi, “A Multimodal Biometric Authentication System for Person Identification and Verification using Fingerprint and Face Recognition” International Journal of Computer Applications (0975 – 8887) Volume 51– No.17, August 2012
[2] R. W. Picard, “Content access for image/video coding: “The Fourth Criterion”,” Tech. Rep. 295, MIT Media Lab, Perceptual Computing, Cambridge, MA, 1994.
[3] R. Brunelli, Template Matching Techniques in Computer Vision: Theory and Practice, Wiley, ISBN 978-0-470-51706-2, 2009
[4] Perlovsky L. I. et.al., Neural Networks and Intellect: Using ModelBased Concepts. New York, NY: Oxford University Press; (3rd printing), 2001.
[5] Crawford, Mark. "Facial recognition progress report". SPIE Newsroom. Retrieved 2011-10- 06.
[6] Liu Y, Simon JD (February 2005). "Metal-ion interactions and the structural organization of Sepia eumelanin". Pigment Cell Res. 18 (1): 42–8. doi:10.1111/j.1600-0749.2004.00197.x. PMID 15649151.
[7] HRSID Iris Recognition | "more than 200 points that can be used for comparison, including rings, furrows and freckles" "Iris scanners 'can be tricked'". "Biometric Identity Management System". UNHCR. Retrieved 2015-11-02.
[8] Wasserman, Philip (2005-12-26). "Solid-State Fingerprint Scanners - A Survey of Technologies" (PDF). Retrieved 2015-1018.
[9] Setlak, Dale. "Advances in Biometric Fingerprint Technology are Driving Rapid Adoption in Consumer Marketplace". AuthenTec. Retrieved 4 November 2010. M. N. Do, M. Vetterli, (2005) “The contourlet transform: an efficient directional multiresolution image representation”, IEEE Transactions on Image Processing, Vol. 14, No. 12, pp. 2091-2106.
[10] Liu, Z., Blasch, E., Xue, Z., Langaniere, R., and Wu, W., (2012). Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(1), 94-109.
[11] Martin, Zach (2011-03-23). "Biometric Trends: Will emerging modalities and mobile applications bring mass adoption?". SecureIDNews. Retrieved 2013-07-14
[12] "Biometric Identity Management System". UNHCR. Retrieved 2015-11-02.
[13] Wasserman, Philip (2005-12-26). "Solid-State Fingerprint Scanners - A Survey of Technologies" (PDF). Retrieved 2015-1018.
[14] Hosseini, M.S.; Araabi, B.N.; Soltanian-Zadeh, H. (April 2010). "Pigment Melanin: Pattern for Iris Recognition". IEEE Trans Instrum Meas 59 (4): 792–804. doi:10.1109/TIM.2009.2037996
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
