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A COMPARISON OF MULTIPLE WAVELET ALGORITHMS FOR IRIS RECOGNITION-2

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A COMPARISON OF MULTIPLE WAVELET ALGORITHMS FOR IRIS RECOGNITION-2 Powered By Docstoc
					  International Journal of JOURNAL OF COMPUTER (IJCET), ISSN 0976-
 INTERNATIONALComputer Engineering and Technology ENGINEERING
  6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME
                           & TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)                                                   IJCET
Volume 4, Issue 2, March – April (2013), pp. 386-395
© IAEME: www.iaeme.com/ijcet.asp
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  A COMPARISON OF MULTIPLE WAVELET ALGORITHMS FOR IRIS
                      RECOGNITION

                               Sayeesh1, Dr. Nagaratna P. Hegde 2
   1
       Asst. Professor, Dept. of CS & E, Alva’s Institute of Engg. & Tech., Shobhavana Campus,
                               Mijar, Moodbidri – 574 225, Karnataka State.
                  2
                    Professor, Dept. of CS & E, Vasavi College of Engg., Ibrahimbhag,
                                     Hyderabad – 31, Andhra Pradesh.



  ABSTRACT

          Personal identification has become the need of modern day life. The identification
  must be fast, automatic and foolproof. Biometrics has emerged as a strong alternative to
  identify a person compared to the traditional ways. Also biometric identification can be made
  fast, automatic and is already foolproof. Among other biometrics, Iris recognition has
  emerged as a strong way of identifying any person. Iris recognition is one of the newer
  biometric technologies used for personal identification. It is one of the most reliable and
  widely used biometric techniques available. In general, a typical iris recognition method
  includes capturing iris images, testing iris liveness, image segmentation, and image
  recognition using traditional and statistical methods. Each method has its own strengths and
  limitations. In this paper, we compare the performance of various wavelets for Iris
  recognition like complex wavelet transform, Gabor wavelet, and discrete wavelet transform.

  Keywords- Iris recognition, complex wavelets, Gabor wavelets, discrete wavelet transform.

  I.        INTRODUCTION

      The demand for security systems is increasing day by day. Rigorous search for different
  verification and identification techniques is the need of the day. Most traditional methods of
  security require a person to possess some type of physical possession, such as a key, or to
  know certain information, such as a password. These techniques are not as secure as
  organizations may desire. In recent years, the increasing capabilities of computers have
  allowed more sophisticated and intelligent personal identification methods. Biometric


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techniques, which use uniquely identifiable physical or behavioral characteristics to identify
individuals, are one such method. Commonly used biometric features are the face,
fingerprints, voice, DNA, retina, and the iris. Iris recognition is regarded as the most reliable
and accurate biometric identification system available. Iris recognition is a biometric-based
method of identification. This method has many advantages, such as unique, stability, can be
collected, nonaggressive, etc. The iris recognition's error rate is the lowest in most biometric
identification method. Now many research organizations at home and abroad spend a lot of
time and energy to do research of iris recognition. The human iris is an annular part between
the pupil (generally appearing black in image) and white sclera has an extraordinary
structure. The iris begins to form in the third month of gestation and structures creating its
pattern are largely complete by the eight months, although pigment accretion can continue in
the first postnatal years. The word iris is generally used to denote the colored Portion of the
eye. It is a complex structure comprising muscle, connective tissues and blood vessels. The
image of a human iris thus constitutes a plausible biometric signature for establishing or
confirming personal identity. Further properties of the iris that makes it superior to finger
prints for automatic identification systems include, among others, the difficulty of surgically
modifying its texture without risk, its inherent protection and isolation from the physical
environment, and it's easily monitored physiological response to light. Additional technical
advantages over fingerprints for automatic recognition systems include the ease of registering
the iris optically without physical contact beside the fact that its intrinsic polar geometry does
make the process of feature extraction easier. It involves using photographs of a person’s
eye(s) to determine the identity of the individual. The iris contains unique features, such as
stripes, freckles, coronas, etc., collectively referred to as the texture of the iris. This texture is
analyzed and compared to a database of images to obtain a match. The probability of a false
match is close to zero, which makes iris recognition a very reliable method of personal
identification.
         This paper discusses performance of different wavelet based algorithm for iris image
enhancement, noise reduction, feature extraction, and matching.

II.     RELATED LITERATURE

        A biometric system provides the automatic recognition of an individual based on
some unique feature or characteristic possessed by the individual. This section describes the
overview of the iris recognition system, theoretical background about wavelets, and
principles of iris recognition.

A. Overview of the Iris Recognition system
        Image processing techniques can be employed to extract the unique iris pattern from a
digitized image of the eye and encode it into the biometric template, which can be stored in
database. This biometric template contains an objective mathematical representation of the
unique information stored in the iris, and allows the comparisons made between templates.
When a person wishes to be identified by an iris recognition system, their eye is first
photographed and then template is created for their iris region. This template is then
compared with the template stored in a database, until either a matching template is found
and a subject is identified, or no match is found and subject remains unidentified.
Human iris recognition process is basically divided into four steps.



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i) Localization: Inner and outer boundaries of the iris are extracted.
ii) Normalization: Iris of different people may be of different size. For the same person, the size
may vary because of changes in the illumination and other factors. So, normalization is
performed to get all the images in a standard form suitable for the processing.
iii) Feature extraction: Iris provides abundant texture information; a feature vector is formed
which consists of the ordered sequence of features extracted from the various representations of
the iris images.
iv) Matching: Feature vectors are classified through euclidean Distance.

B. Wavelets
        Addison describes a wavelet as a mathematical function used to divide a given function or
a continuous-time signal into different frequency components and study each component with a
resolution that matches its scale. The wavelets are scaled and are the translated copies (known as
“daughter wavelets”) of a finite-length or fast-decaying oscillating waveform (known as the
“mother wavelet”). Wavelet transforms have advantages over traditional Fourier transforms for
representing functions that have discontinuities and sharp peaks, and for accurately
deconstructing and reconstructing finite, non-periodic, and/or non-stationary signals. These
underlying characteristics make wavelets applicable for creating the feature vector that is
necessary in the iris recognition algorithm.

C. Iris Recognition Algorithms and Principles
         Many algorithms have been developed for the iris recognition system. The wavelet
functions or wavelet analysis is a recent solution for overcoming the shortcomings in image
processing, which is crucial for iris recognition. Nabti and Bouridane proposed a novel
segmentation method based on wavelet maxima and a special Gabor filter bank for feature
extraction, which obtains an efficient recognition with an accuracy of 99.43%. The steps are as
follows: the multi-scale edge detection method is used for iris image processing, the extraction of
features from an iris-polarized image using the proposed Gabor filter bank, and matching with
Hamming distance for identification and recognition. Narote et al. proposed a new algorithm for
iris recognition based on the Dual Tree Complex Wavelet Transform. The Dual Tree Complex
Wavelet Transform (DTCWT) provides three significant advantages: they have reduced shift
sensitivity with low redundancy, improved directionality, and explicit phase information.
Experimental results show that the above algorithm based on DTCWT is nearly 25 times faster
than that of Narote. Also, the authentication using DTCWT demonstrates that the approach is
promising in terms of improving iris-based identification.

III.     IRIS RECOGNITION SYSTEM

         A typical Iris Recognition system basically consists of following main modules as shown
below,
                                                                                    Feature
                         Image            Eye        Image          Image          Extraction
                       Acquisition       Image    Segmentation   Normalization         &
                                                                                   Matching




                                                                     Iris        Recognition
                                                                   Feature
                                                                   database




                                     Figure 1. Iris Recognition System

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A. Image Acquisition
        It is to capture a sequence of iris images from the person concerned using a
specifically designed sensor. Since, the iris is fairly small and exhibits abundant features
under infrared lighting, capturing iris images of high quality is one of the major challenges
for practical applications. While designing an image acquisition apparatus the factors that
must be taken into consideration is the lighting system, the positioning systems and physical
capture system.

B. Iris Segmentation
        The next stage of iris recognition is to isolate actual iris region in an eye image, the
eyelids and eyelashes normally occlude the upper and lower parts of the iris region. Also,
specular reflections can occur within the iris region corrupting the iris pattern. A technique is
required to isolate and exclude these artifacts as well as locating the circular iris region

C. Iris Normalization
        The normalization process will produce iris regions, which have the same constant
dimensions, so that two photographs of the same iris under different conditions will have
characteristic features at the same spatial location. Another point of note is that the pupil
region is not always concentric within the iris region, and is usually slightly nasal. This must
be taken into account while trying to normalize the ‘doughnut’ shaped iris region to have
constant dimensions. The rubber sheet model takes into account pupil dilation and size
inconsistencies in order to produce a normalized representation with constant dimensions.

IV. WAVELET BASED ALGORITHMS

        Wavelet transforms are used to extract the feature of normalized iris image, wavelet
coefficients vectors are used as a feature for iris recognition, four types of wavelet
coefficients e.g. vertical, horizontal, approximate and detail can be used, here simple Harr
wavelet is used,




                  Figure 2: (a) Horizontal (b) Vertical (c) Approximate and
              (d) Detail coefficients of Haar wavelet transform for iris template

Wavelet transform has three main disadvantages, Shift sensitivity, Poor directionality and
Absence of phase information, these disadvantages can be overcome by complex wavelet.




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A. Complex Wavelets
       Complex Wavelets Transforms use complex valued filtering that decomposes the
real/complex signals into real and imaginary parts in transform domain. The real and
imaginary coefficients are used to compute amplitude and phase information, just the type of
information needed to accurately describe the energy localization of oscillating functions.
Here complex frequency B-spline wavelet is used for iris feature extraction A complex
frequency B-spline wavelet is defined by




        Figure 3: Complex Frequency B-Spline wavelet coefficients for iris template

B. Gabor Wavelet
         The main idea of this method is that: firstly we construct two-dimensional Gabor
filter, and we take it to filter these images, and after we get phase information, code it into
2048 bits, i.e. 256 bytes. In image processing, a Gabor filter, named after Dennis Gabor, is a
linear filter used for edge detection. Frequency and orientation representations of Gabor filter
are similar to those of human visual system, and it has been found to be particularly
appropriate for texture representation and discrimination. In the spatial domain, a 2D Gabor
filter is a Gaussian kernel function modulated by a sinusoidal plane wave. The Gabor filters
are self-similar – all filters can be generated from one mother wavelet by dilation and
rotation.
         Its impulse response is defined by a harmonic function multiplied by a Gaussian
function. Because of the multiplication-convolution property (Convolution theorem), the
Fourier transform of a Gabor filter's impulse response is the convolution of the Fourier
transform of the harmonic function and the Fourier transform of the Gaussian function. The
filter has a real and an imaginary component representing orthogonal directions. The two
components may be formed into a complex number or used individually.
         Gabor filters are directly related to Gabor wavelets, since they can be designed for a
number of dilations and rotations. However, in general, expansion is not applied for Gabor
wavelets, since this requires computation of bi-orthogonal wavelets, which may be very time-
consuming. Therefore, usually, a filter bank consisting of Gabor filters with various scales
and rotations is created. The filters are convolved with the signal, resulting in a so-called
Gabor space. This process is closely related to processes in the primary visual cortex. Jones
and Palmer showed that the real part of the complex Gabor function is a good fit to the
receptive field weight functions found in simple cells in a cat's striate cortex.


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        The Gabor space is very useful in image processing applications such as optical
character recognition, iris recognition and fingerprint recognition. Relations between
activations for a specific spatial location are very distinctive between objects in an image.
Furthermore, important activations can be extracted from the Gabor space in order to create a
sparse object representation.
Local regions of an iris are projected onto quadrature 2-D Gabor wavelets using equation (1).




        Where            is a complex-valued bit whose real and imaginary parts are either 1or
0 (sign) depending on the sign of the 2-D integral;               is the raw iris image in a
dimensionless polar coordinate system that is size and translation-invariant; α and β are the
multi scale 2-D wavelet size parameters, spanning an 8-fold range from 0.15 mm to 1.2 mm
on the iris; ω is wavelet frequency, spanning 3 octaves in inverse proportion to β;
represents the polar coordinates of each region of iris for which the phasor coordinates h(
Re,Im ), like figure 4.
        Equation (1) generates complex-valued projection coefficients whose real and
imaginary parts specify the coordinates of a phasor in the complex plane. The angle of each
phasor is quantized to one of the four quadrants, setting two bits of phase information. This
process is repeated all across the iris with many wavelet sizes, frequencies, and orientations
to extract 2,048 bits, i.e. 256 bytes. Such a phase quadrant coding sequence is illustrated for
one iris by the bit stream shown graphically in Figure 4.
After feature extraction of figure 1, get figure 6 & 7.




                       Figure 4. Phase-Quadrant Demodulation Code


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                            Figure 5. Normalized Unwrapped Iris




                                 Figure 6. Real Component




                              Figure 7. Imaginary Component

C. The Discrete Wavelet Transform
        Computing wavelet coefficients at every possible scale is a fair amount of work, and
it generates an awful lot of data. That is why we choose only a subset of scales and positions
at which to make our calculations. It turns out, rather remarkably, that if we choose scales
and positions based on powers of two so-called dyadic scales and positions then our analysis
will be much more efficient and just as accurate. We obtain such an analysis from the discrete
wavelet transform (DWT) given by (1).




        An efficient way to implement this scheme using filters was developed in 1988. This
algorithm is in fact a classical scheme known in the signal processing community as a two-
channel sub band coder. This very practical filtering algorithm yields a fast wavelet transform
a box into which a signal passes, and out of which wavelet coefficients quickly emerge. Let’s
examine this in more depth.

Let,




Both        and        can be expressed as linear combinations of double-resolution copies of
themselves.

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Here      in (2)    in (3) the expansion coefficients are called scaling and wavelet vectors,
respectively. They are the filter coefficients of fast wavelet transform (FWT),
             Approximate coefficients
           Horizontal coefficients
            Vertical coefficients

            Diagonal coefficients
Here            is the original image whose DWT is to be computed.

V.     EXPERIMENTATION AND RESULTS

A. Complex Wavelets
        The iris templates are matched using different angles 210,240,280,320 and 350
degrees and it is observed that as angles increases percentage of matching also increases the
better match is observed at angle 350 which is 93.05%.Further by detecting eyelids and
eyelashes the iris image is cropped and iris template is generated for matching purpose the
results obtained is better than previous results the matching score is 95.30%.




            Figure 8. Graph for angles verses matching percentage of iris images

B. Gabor Wavelet
        We use images of eyes from 10 persons, and every person has six images of eyes. The
top three images are used as test images and the next three images are used for training
purpose. We use the Daugman’s methods to iris regions segmentation and use Gabor wavelet
for feature extraction. At last, in the identification stage we calculate Hamming distance
between a test image & a training image. The smallest distance among them is expressed, that
test image belongs to this class. The recognition rate is 96.5%.

C. The Discrete Wavelet Transform:
       The technique developed here uses all the frequency resolution planes of Discrete
Wavelet Transform (DWT). These frequency planes provide abundant texture information
present in an iris at different resolutions. The accuracy is improved up to 98.98%. With
proposed method FAR and FRR is reduced up to 0.0071% and 1.0439% respectively.



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VI.     CONCLUSION

        In this paper, we compare the performance of various wavelets for Iris recognition
like complex wavelet transform, Gabor wavelet, and discrete wavelet transform. Using
complex wavelet, different coefficient vectors are calculated. Minimum distance classifier
was used for final matching. The smaller the distance the more the images matched. It is
observed that for the complex wavelets the results obtain are good than the simple wavelet
because in complex wavelet we get both phase and angle also real and imaginary coefficients,
so we can compare all these parameters for iris matching purpose.2D Gabor wavelets have
the highest recognition rate. Because iris is rotator, and 2D Gabor wavelets have rotation
invariance, it has the highest recognition rate. But 2D Gabor wavelets have high
computational complexity, and need more time. Discrete wavelet transform used for iris
signature formation gives better and reliable results.

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