iris is high protected and non-invasive and ideal for handling applications
requiring management of large user groups. Although small and some problematic
to an image, where the iris has a greater mathematical advantage of its pattern
variation among different persons.
3. IMAGE PREPROCESSING:
preprocessing is the common name for operations with images at a lowest level
of abstraction both input and output of the intensity images. Preprocessing is
a method mainly given the enhanced output of the input image. The aim of the
preprocessing is an improvement of the image date that suppresses the unwanted
distortions. While, taking a raw image it consists of noise and some intensity
difference, major distortion to avoiding this, the preprocessing method is
important in image processing. Preprocessing step is performed to minimize
noise as possible as well as to resizing the original image. After this process
there is no noise consisting an image and the image is smoothed.
which includes many steps for an example noise removal, converting into a gray
scale image, normalization, localization and etc. Image preprocessing methods use
to considerably redundancy in images reduces the noise and improve (or) increase
number of the pixels of the dataset.
this paper the preprocessing includes the converting into grayscale image from
the raw image and noise removal of the input image.
3.1 Converting into gray scale image:
this paper I have used the converting into gray scale image and noise removal
in preprocessing method. In real time the iris images consists of primary
colors it is very difficult to get the result and it has complex computational
process as well. Because primary colors have its own property and if we
concentrate on that, the process goes too big and difficult and it takes much
time. To overcome, this issue the gray scale images has been uses in the
preprocessing method. Converting an image into a gray image, it gives the range
of shades of gray without apparent color.
this method is very much important in the preprocessing because the
computational steps of the gray scale image is simple and easy to use for
further process in image processing. After the conversion the image consists
only two colors (Black and white). Where it may represented in the form of
binary (0 and 1) and decimal(0 to 255) values.
gray scale image is depends on the amount of primary colors involving in the
input image. And output of gray image is equal to the input of the primary
image. so there is no more information is loss in this method. Only the image is
converted into a gray scale image.
color is represented in the gray scale is the darkest possible shade, which is
the total absence of transmitted or reflected light and the white color is
defined in form of the light possible shade, which is total presence of
transmitted or reflected light.
with primary colors the black and white is represented by R=G=B = 0 or R=G=B
=00000000 and R=G=B =1 or R=G=B =11111111 for an 8bit gray scale image. This
method is also known as black and white image.
three main parameters are defined in the gray scale image that is saturation,
hue, brightness. In each pixel the saturation and hue is equal to 0 but the
brightness is only the parameter can vary from min of 0(black) to max of
3.2 NOISE REMOVAL:
The 2dimensional discrete wavelet
transform is used to extract the specified features or information from the
enhanced image. The main purpose of the 2D DWT is to select and extract the
important features from image. The feature vector is formed which consists of
the ordered sequences of the features extracted from the various representation
of the iris image; which will done by the wavelet technique. I have applied 2D
DWT technique in this and there are many wavelet mask, in addition I used only
one type which is harr wavelet transform masks which gives the adequate results
in this method. Using the wave menu command in matlab function the enhanced
image is given the output image by using the harr masks with the level of four.
We can use different levels in this for the better extraction. And another
important uses of the 2D DWT is we just take the (LL) sub-band for the storage
The histogram equalization is the method of technique
for adjusting image intensities to enhance contrast and there is no theory behind this. Image
enhancement is a technique in image processing that uses in preparing an image
for a particular application. The resultant of this process gives out the high
performance technique in one application might not be useful in another
application. Generally there are two approaches in image enhancement technique
that is spatial and frequency domain.
In this paper I have only used the very
common technique HE. It enhances overall contrast of an image by transforming
an original image into a uniform histogram. The HE is used to enhance the image
and gives the output image in the form of graphical representation. From this
enhancement we can get the some accurate portion of the image.
In modern days, the field of digital image
processing the image enhancement plays a vital role. Therefore, a several
enhancement technique has been proposed and used in image processing to improve
the quality of the image.
Up to date, there is no best method is to
enhance the image than the histogram equalization. This method is very simple and
effective method among other enhancement method.
In my work based on the histogram
equalization, the output of the images explain the detail information of the
iris image. Where the stretched iris image which gives the better resolution
than normal iris image. It will helpful for the further process and we can
easily identified the difference in particular and specified image.