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

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630 Z.Yang,Z.Yang&L.Yang 210 200 190 180 170 160 150 140 130 120 320 340 360 380 400 ϖϖ 420 440 210 200 190 180 170 160 150 140 130 120 320 340 360 380 400 420 440 Fig. 5. Left, the original signal (solid line) and the third IMF (dash line) in the interval [320, 450]. Right, the original signal (solid line) and the fourth IMF (dash line) in [320, 450]. Frequency Content 700 600 500 400 300 200 100 0 0 0.038 0.068 0.1 0.2 Frequency (Hz) 0.3 0.4 0.45 Frequency Content 700 600 500 400 300 200 100 0 0 0.036 0.069 0.1 0.2 Frequency (Hz) 0.3 0.4 0.45 Frequency Content 700 600 500 400 300 200 100 f C 0 0 0.051 0.1 0.2 Frequency (Hz) 0.3 0.4 0.45 Fig. 6. Left, the Hilbert marginal spectrum of the 11th line signal of the normalized iris in Fig. 3. Middle, the Hilbert marginal spectrum of the 12th line signal. Right, the average Hilbert marginal spectrum and the main frequency center fC of the line signal series in the first subregion of the normalized iris in Fig. 3. that the main frequency information of signals along the same direction in the same subregion of an iris is similar. As an example, we compute the Hilbert marginal spectrum of the 12th line signal of the normalized iris image in Fig. 3, as shown in the middle of Fig. 6. It can be seen that the Hilbert marginal spectrums of the 12th signal is very similar with that of the 11th signal. Then we compute the average Hilbert marginal spectrum of the signal series along the horizontal direction in the first subregion of Fig. 3, as shown in the right of Fig. 6. It can be seen that it is not only coincident with the Hilbert marginal spectrum of each line but also more concentrated. Therefore, based on the average Hilbert marginal spectrum of the line signal series in each subregion, we can obtain the main frequency center fC described as Eq. (5) as a reliable feature of the iris. Since the orientation information is a very important pattern in an iris, 14,24 the main frequency center along the horizontal direction is not only enough to characterize its texture information. To characterize the orientation information, the features along the other directions should be considered. First of all, we should
α ( ) α Iris Recognition Based on Hilbert–Huang Transform 631 Fig. 7. The generation of the signal along angle α of each subregion: choose one point of the first column and connect all the dotted lines. present the generation of signal series along angle α of each subregion of an iris. The generation method can be simply described as follows. As shown in Fig. 7, the rectangle frame denotes one of the subregion of an iris. Firstly choose one point of the first column and connect all the dotted lines. Then we obtain one signal along angle α of the subregion. Similarly, we can generate all the signal series (totally 16 signals) along angle α in the subregion. In experiments, we totally choose 18 directions: 0 ◦ , 10 ◦ ,...,170 ◦ . It is found that it has a good classification performance when the main frequency centers described as Eq. (5) are used as features for iris recognition. The features can cluster the samples of same class of iris. As an example, we show three samples of the same iris and their main frequency centers of 18 directions in I1 in Fig. 8. It can be seen that the features cluster very well. Furthermore, the gaps are usually existent for the samples from different classes of iris images. This implies that the selected features have really a good classification ability for the iris images. It is also found that the energy e of the main frequency center is also a good feature for classification. It can reflect the image contrast of different classes of iris. The higher the contrast is, the larger energy the image has. In other words, a signal will have a larger energy if it waves in a larger amplitude. Thus, a larger energy in marginal spectrum can be expected if an iris has the higher contrast. It can be seen from Fig. 9 that in these three iris classes (a), (b) and (c), the class (a) has the highest contrast while the class (c) has the lowest contrast. It indicates that the class (a) has generally the largest energy while the class (c) has the smallest energy of the main frequency center along the same orientation. An encouraging result is received as shown in Fig. 9(d). It implies that the energies of the main frequency center should be also used as features to classify the irises. As is known, that we choose the main frequency center and its energy as features, now the feature extraction algorithm is presented as follows. Algorithm 1 (Feature extraction algorithm). Given a normalized iris image I(i, j), let its three subregions from the top down divided as Fig. 3 be I1, I2 and I3. D = {di|di =(i − 1)10,i=1, 2,...,18} — the feature orientations. . . . . . .
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: ROI { Iris Recognition Based on Hil
Page 11 and 12: Energy 600 400 200 Iris Recognition
Page 13 and 14: x c1 c2 c3 c4 c5 Iris Recognition B
Page 15 and 16: Main Frequency Center 0.06 0.05 0.0
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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