Conference Paper2012

Fundus phase congruency based biometrics system

Mislamuddin Ahmed, Bruce Poon, MD Jewel, M Ashraful Amin, Hong Yan

2012 International Conference on Machine Learning and Cybernetics

IEEE, Vol. 5, pp. 1668–1674

DOI: 10.1109/icmlc.2012.6359625URL

Abstract

This paper presents a novel biometric authentication method using retinal fundus images. Phase congruency is computed on both RGB and YCbCr channel for vessel segmentation and the Fourier components are used to detect edges. By applying pair threshold values on the phase congruent image, retinal blood vessel tree is acquired. Three different features are used and all combinations of the features are experimented to find which combination produces the best authentication accuracy. Two separate experiments are done, EXP-1 using 18 images from 6 individuals and EXP-2 using 18 (authorized) plus 547 (intruder) images, each from a separate individual. For similarity matching, 2-D correlation coefficient measure is used. In EXP-1 and EXP-2, maximum accuracy achieved was 94.44% and 93.4% respectively and both with YCbCr images. YCbCr color space outperformed RGB color space by a small margin. For EXP-l and EXP-2, the average time taken per image was 12.81 and 12.92 seconds respectively.

Keywords

  • Phase congruency
  • biometric authentication
  • retinal vessel segmentation
  • YCbCr color space
  • feature combination

CCDS Authors

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

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