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

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636 Z.Yang,Z.Yang&L.Yang x c1 c2 c3 c4 c5 c6 r 250 200 150 (a) (b) 0 20 0 50 100 150 200 250 300 350 400 450 500 −20 0 20 0 50 100 150 200 250 300 350 400 450 500 −20 0 20 0 50 100 150 200 250 300 350 400 450 500 −20 0 20 0 50 100 150 200 250 300 350 400 450 500 −20 0 10 0 50 100 150 200 250 300 350 400 450 500 −10 0 10 0 50 100 150 200 250 300 350 400 450 500 −10 0 200 180 50 100 150 200 250 300 350 400 450 500 160 0 50 100 150 200 250 300 350 400 450 500 x c1 c2 c3 c4 c5 c6 r 200 150 100 50 0 20 0 50 100 150 200 250 300 350 400 450 500 −20 0 20 0 50 100 150 200 250 300 350 400 450 500 −20 0 20 0 50 100 150 200 250 300 350 400 450 500 −20 0 20 0 50 100 150 200 250 300 350 400 450 500 −20 0 10 0 50 100 150 200 250 300 350 400 450 500 −10 0 10 0 50 100 150 200 250 300 350 400 450 500 −10 0 150 50 100 150 200 250 300 350 400 450 500 100 0 50 100 150 200 250 300 350 400 450 500 (c) (d) (e) (f) 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.
Main Frequency Center 0.06 0.05 0.04 0.03 0.02 0 20 40 60 80 Orientation 100 120 140 160 Iris Recognition Based on Hilbert–Huang Transform 637 Original features Noise features (a) Energy 500 400 300 200 Original features Noise features 0 20 40 60 80 Orientation 100 120 140 160 (b) (c) Fig. 12. (a) The original iris image; (b) the main frequency centers of 18 orientations in I1 of the original normalized image (“o”) and those of the noisy normalized image (“•”); (c) the energies of 18 orientations in I1 of the original normalized image (“o”) and those of the noisy normalized image (“•”). 4. Experimental Results To evaluate the performance of the proposed method, we applied it to the widely used database named CASIA iris database. 1 The database includes 2255 iris images from 306 different eyes (hence, 306 different classes). The captured iris images are 8-bit gray images with a resolution of 320 × 280. 4.1. Performance of the proposed method For each iris class, we choose three samples taken at the first session for training and all samples captured at the second and third sessions serve as test samples. Therefore, there are 918 images for training and 1337 images for testing. Figure 13(a) describes variations of the correct recognition rate (CRR) with changes of dimensionality of the reduced feature vector using the LDA. From this figure, we can see that with increasing dimensionality of the reduced feature vector, the recognition rate also increases rapidly. However, when the dimensionality of the reduced feature
Page 1 and 2: Advances in Adaptive Data Analysis
Page 3 and 4: Iris image preprocessing Iris image
Page 5 and 6: Iris Recognition Based on Hilbert-H
Page 7 and 8: ROI { Iris Recognition Based on Hil
Page 9 and 10: α ( ) α Iris Recognition Based on
Page 11 and 12: Energy 600 400 200 Iris Recognition
Page 13: x c1 c2 c3 c4 c5 Iris Recognition B
Page 17 and 18: False Reject Rate(%) 0 2.5 2 1.5 1
Page 19: Iris Recognition Based on Hilbert-H

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

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