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

626 Z.Yang,Z.Yang&L.Yang
(a) (b)
(c)
Fig. 2. Iris image preprocessing: (a) original image; (b) localized image; (c) normalized image.
Hough transform again in a certain region determined by the center of the pupil.
A localized image is shown in Fig. 2(b).
Irises from different people may be captured in different sizes and, even for
irises from the same eye, the size may change due to illumination variations and
other factors. It is necessary to compensate for the iris deformation to achieve more
accurate recognition results. Here, we counterclockwise unwrap the iris ring to a
rectangular block with a fixed size (64 × 512 in our experiments). 6,14 That is, the
original iris in a Cartesian coordinate system is projected into a doubly dimensionless
pseudopolar coordinate system. The normalization not only reduces to a
certain extent distortion caused by pupil movement but also simplifies subsequent
processing. A normalized image is shown in Fig. 2(c).
3. Feature Extraction and Matching
3.1. The Hilbert–Huang Transform
The Hilbert–Huang Transform (HHT) was proposed by Huang et al., 11 which is
an important method for signal processing. It consists of two parts: the empirical
mode decomposition (EMD) andtheHilbert spectrum. With EMD, any complicated
data set can be decomposed into a finite and often small number of intrinsic mode
functions (IMFs). An IMF is defined as a function satisfying the following two
conditions: (1) it has exactly one zero-crossing between any two consecutive local
extrema; (2) it has zero local mean.