Fundus phase congruency based biometrics system

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.