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

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.
. . . . . .