IRIS RECOGNITION BASED ON HILBERT–HUANG TRANSFORM 1 ...

636 Z.Yang,Z.Yang&L.Yang
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Fig. 11. (a) Iris image with bright illumination; (b) the same iris image with dark illumination;
(c) the EMD decomposition result of the 10th line of (a); (d) the EMD decomposition result of
the 10th line of (b); (e) the result by removing all the residues of lines from (a); (f) the result by
removing all the residues of lines from (b).
To show the robustness of our method to noise, we added 20 dB Gauss white
noise to the iris image as shown in Fig. 12(a) and calculate the main frequency
centers and the energies of 18 orientations in I1 of the original normalized image
and the noise normalized image, as shown in Figs. 12(b) and 12(c), respectively.
It can be seen that most features of the noisy iris image just have small changes
compared with those of the original iris. Therefore, the proposed feature is robust
to high frequency noise.
3.4. Iris matching
After feature extraction, an iris image is represented as a feature vector of length
108. To improve computational efficiency and classification accuracy, Linear Discriminant
Analysis (LDA) is first used to reduce the dimensionality of the feature
vector and then the Euclidean similarity measure is adopted for classification. LDA
is a linear statistic classification method, which intends to find a linear transform T
as such that, after its application, the scatter of sample vectors is minimized within
each class, and the scatter of those mean vectors around the total mean vector can
be maximized simultaneously. Further details of LDA may be found in Ref. 8.