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

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624 Z.Yang,Z.Yang&L.Yang organ. All these desirable properties make iris recognition suitable for highly reliable personal identification. For the last decade, a number of researchers have worked on iris recognition and have achieved great progress. According to the various feature extractions, existing iris recognition methods can be roughly divided into four major categories: the phase-based methods, 6,7,23 the zero-crossing representation-based methods, 4,22 the texture analysis-based methods 14,24 and intensity variation analysis methods. 15,16 A phase-based method is a process of phase demodulation. Daugman 6,7 made use of multiscale Gabor filters to demodulate texture phase structure information of the iris. Then the filter outputs were quantized to generate a 2048-bit iriscode to describe an iris. Tisse et al. 23 encoded the instantaneous phase and emergent frequency with the analytic image (two-dimensional Hilbert transform) as iris features. The zero-crossings of wavelet transform provide meaningful information of image structures. Boles and Boashash 4 calculated zero-crossing representation of one-dimensional wavelet transform at various resolution levels of a virtual circle on an iris image to characterize the texture of the iris. Wildes et al. 24 represented the iris texture with a Laplacian pyramid constructed with four different resolution levels. Tan et al. 14 proposed a well-known texture analysis method by capturing both global and local details from an iris with the Gabor filters at different scales and orientations. As an intensity variation analysis method, Tan et al. constructed a set of one-dimensional intensity signals to contain the most important local variations of the original two-dimensional iris image. Then the Gaussian–Hermite moments of such intensity signals are used as distinguishing features. 16 Fourier and Wavelet descriptors have been used as powerful tools for feature extraction which is a crucial processing step for pattern recognition. However, the main drawback of those methods is that their basis functions are fixed and do not necessarily match the varying nature of signals. Hilbert–Huang transform (HHT) developed by Huang et al. is a new analysis method for nonlinear and nonstationary data. 11 It can adaptively decompose any complicated data set into a finite number of intrinsic mode functions (IMFs) that become the bases representing the data by empirical mode decomposition (EMD). With Hilbert transform, the IMFs yield instantaneous frequencies as functions of time. The final presentation of the results is a time–frequency–energy distribution, designated as the Hilbert spectrum that gives sharp identifications of salient information. Therefore, it brings not only high decomposition efficiency but also sharp frequency and time localizations. Recently, the HHT has received more attention in terms of interpretations 9,18,21 and applications. Its applications have spread from ocean science, 10 biomedicine, 12,20 speech signal processing, 25 image processing, 3 pattern recognition 19,26,28 and so on. Recently, EMD is also used for iris recognition as a low pass filter in Ref. 5. Since a random iris pattern can be seen as a texture, many well-developed texture analysis methods have been adapted to recognize the iris. 14,24 An iris consists of some basic elements which are similar to each other and interlaced each other,
Iris image preprocessing Iris image Localization Normalization Iris Recognition Based on Hilbert–Huang Transform 625 Feature extraction Classification HHT LDA Fig. 1. Diagram of the proposed method. Output i.e. an iris image is generally periodical to some extent. Therefore the approximate period is an effective feature for the iris recognition. By employing the main frequency center presented in our previous works 26,27 of the Hilbert marginal spectrum as an approximation for the period of an iris image, a new iris recognition method based on HHT is proposed in this paper. Unlike directly using the residue of the EMD decomposed iris image for recognition in Ref. 5, the proposed method utilizes the main frequency center information as the feature vector which is particularly rotation invariant. In comparison with the existing iris recognition methods, the proposed algorithm has an excellent percentage of correct classification, and possesses very nice properties, such as translation invariance, scale invariance, rotation invariance, illumination invariance and robustness to high frequency noise. Figure 1 illustrates the main steps of our method. The remainder of this paper is organized as follows. Brief descriptions of image preprocessing are provided in Sec. 2. A new feature extraction method and matching are given in Sec. 3. Experimental results and discussions are reported in Sec. 4. Finally, conclusions of this paper are summarized in Sec. 5. 2. Iris Image Preprocessing An iris image, contains not only the iris but also some irrelevant parts (e.g. eyelid, pupil, etc.). A change in the camera-to-eye distance may also result in variations in the size of the same iris. Therefore, before feature extraction, an iris image needs to be preprocessed to localize and normalize. Since a full description of the preprocessing method is beyond the scope of this paper, such preprocessing is introduced briefly as follows. The iris is an annular part between the pupil (inner boundary) and the sclera (outer boundary). Both the inner boundary and the outer boundary of a typical iris can approximately be taken as circles. This step detects the inner boundary and the outer boundary of the iris. Since the localization method proposed in Ref. 14 is a very effective method, we adopt it here. The main steps are briefly introduced as follows. Since the pupil is generally darker than its surroundings and its boundary is a distinct edge feature, it can be found by using edge detection (Canny operator in experiments). Then a Hough transform is used to find the center and radius of the pupil. Finally, the outer boundary will be detected by using edge detection and
Page 1: Advances in Adaptive Data Analysis
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Page 15 and 16: Main Frequency Center 0.06 0.05 0.0
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IRIS RECOGNITION BASED ON HILBERT–HUANG TRANSFORM 1 ...

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