**INTRODUCTION:**

A biometric
system provides automatic recognition of an individual based on some sort of
unique feature or characteristic possessed by the individual. Biometric systems
have been developed based on fingerprints, facial features, voice, hand geometry,
handwriting, the retina, and the one presented in this thesis, the iris.
Biometric systems work by first capturing a sample of the feature, such as
recording a digital sound signal for voice recognition, or taking a digital color
image for face recognition.

The sample is then transformed using some sort of mathematical function into a biometric template. The biometric template will provide a normalized, efficient and highly discriminating representation of the feature, which can then be objectively compared with other templates in order to determine identity. Most biometric systems allow two modes of operation. An enrollment mode for adding templates to a database, and an identification mode, where a template is created for an individual and then a match is searched for in the database of pre-enrolled templates. A good biometric is characterized by use of a feature that is; highly unique – so that the chance of any two people having the same characteristic will be minimal, stable – so that the feature does not change over time, and be easily captured – in order to provide convenience to the user, and prevent misrepresentation of the feature. The iris is an externally visible, yet protected organ whose unique epigenetic pattern remains stable throughout adult life. These characteristics make it very attractive for use as a biometric for identifying individuals. Image processing techniques can be employed to extract the unique iris pattern from a digitized image of the eye, and encode it into a biometric template, which can be stored in a database. This biometric template contains an objective mathematical representation of the unique information stored in the iris, and allows comparisons to be made between templates. When a subject wishes to be identified by iris recognition system, their eye is first photographed, and then a template created for their iris region. This template is then compared with the other templates stored in a database until either a matching template is found and the subject is identified, or no match is found and the subject remains unidentified. Compared with other biometric technologies, such as face, speech and finger recognition, iris recognition can easily be considered as the most reliable form of biometric technology. However, there have been no independent trials of the technology, and source code for systems is not available. Also, there is a lack of publicly available data sets for testing and research, and the test results published have usually been produced using carefully imaged irises under favorable conditions.

The sample is then transformed using some sort of mathematical function into a biometric template. The biometric template will provide a normalized, efficient and highly discriminating representation of the feature, which can then be objectively compared with other templates in order to determine identity. Most biometric systems allow two modes of operation. An enrollment mode for adding templates to a database, and an identification mode, where a template is created for an individual and then a match is searched for in the database of pre-enrolled templates. A good biometric is characterized by use of a feature that is; highly unique – so that the chance of any two people having the same characteristic will be minimal, stable – so that the feature does not change over time, and be easily captured – in order to provide convenience to the user, and prevent misrepresentation of the feature. The iris is an externally visible, yet protected organ whose unique epigenetic pattern remains stable throughout adult life. These characteristics make it very attractive for use as a biometric for identifying individuals. Image processing techniques can be employed to extract the unique iris pattern from a digitized image of the eye, and encode it into a biometric template, which can be stored in a database. This biometric template contains an objective mathematical representation of the unique information stored in the iris, and allows comparisons to be made between templates. When a subject wishes to be identified by iris recognition system, their eye is first photographed, and then a template created for their iris region. This template is then compared with the other templates stored in a database until either a matching template is found and the subject is identified, or no match is found and the subject remains unidentified. Compared with other biometric technologies, such as face, speech and finger recognition, iris recognition can easily be considered as the most reliable form of biometric technology. However, there have been no independent trials of the technology, and source code for systems is not available. Also, there is a lack of publicly available data sets for testing and research, and the test results published have usually been produced using carefully imaged irises under favorable conditions.

**Youtube video of the Project:****Discrete Cosine Transform (DCT):**

The DCT is a
real valued transform, which calculates a truncated Chebyshev series possessing
well-known mini-max properties and can be implemented using the Discrete Fourier
Transform (DFT). There are several variants but the one most commonly used
operates on a real sequence xn of length N to produce coefficients Ck,

Due to its
strong energy compaction property, the DCT is widely used for data compression.
In addition, the feature extraction capabilities of the DCT coupled with
well-known fast computation technique have made it a candidate for pattern
recognition problems such as the one addressed here. In particular, the DCT has
been shown to produce good results on face recognition, where it has been used
as a less computationally intensive replacement for the Karhunen-Loeve
transform (KLT), which is an optimal technique according to the least squares
metric for projecting a large amount of data onto a small dimensional subspace.
The KLT decomposes an image into principal components ordered on the basis of
spatial correlation and is statistically optimal in the sense that it minimizes
the mean square error between a truncated representation and the actual data.
The DCT, with its variance distribution closely resembling that of the KLT, has
been shown to approach its optimality with much lower computational complexity.
Additionally, its variance distribution decreases more rapidly compared to
other deterministic transforms. Although no transform can be said to be optimal
for recognition, these well-known properties motivated us to investigate the
DCT for effective nonsemantic feature extraction from human iris images.

**Localization:**

Location of
the pupil and outer iris boundaries starts with the removal of the bright spot
in the pupil caused by the reflection of the infrared light source. This
reduces the influence of high gray-level values on the gray-scale distribution.
Then, the image is scanned to isolate a region containing the pupil and iris.
This is done by a heuristic method based on the assumption that the majority of
image rows and columns passing through the pupil will have larger gray-level
variance than those not passing through the pupil. It is assumed that the pupil
is circular and, because the pupil boundary is a distinct edge feature, a Hough
transform is used to find the center and radius of the pupil. To locate the
outer boundary of the iris (limbus), a horizontal line through the pupil center
is scanned for the jumps in gray level on either side of the pupil. The limbus
is normally circular but its center does not necessarily coincide with that of
the pupil.

**Normalization and Enhancement:**

Due to the
dilation and constriction of the human pupil, the radial size of the iris
varies under different illumination conditions and in response to physiological
factors. The resulting deformation of the iris texture can be approximated as a
linear deformation. Since we know the iris boundaries, we can map a rectangular
image array back to an angular and radial position in the iris. This position
will not, in general map exactly onto a pixel in the source image, so the normalized
gray value is obtained by bilinear interpolation from its four nearest
neighbors. Finally, the gray levels are adjusted by removing the peak
illumination caused by light sources reflecting from the eye, estimating and
subtracting the slowly varying background illumination, and equalizing the
gray-level histogram of the iris image. The final normalized image is of
resolution 512x80, from which we code only the 48 rows nearest the pupil to
mitigate the effect of eyelids.

**Feature Extraction:**

As in our
Fourier-based iris coding work, we start from a general paradigm whereby the
feature vectors will be derived from the zero crossings of the differences
between 1D DCT coefficients calculated in rectangular image patches. Averaging
across the width of these patches with appropriate windowing helps to smooth
the data and mitigate the effects of noise and other image artifacts. This then
enables us to use a 1D DCT to code each patch along its length, giving
low-computational cost. The selection of the values for the various parameters
was done by extensive experimentation over the CASIA and Bath databases to
obtain the best predicted Equal Error Rate (EER). The two data sets were used
in their entirety to optimize the parameters of the method. Experimentally,
overlapping patches gave the best EER in combination with the other parameters.
It was also found that horizontally aligned patches worked best, and a rotation
of 45 degrees was better than 0 degrees or 90 degrees. This distinctive feature
of our code introduces a blend of radial and circumferential texture allowing
variations in either or both directions to contribute to the iris code. To form
image patches, we select bands of pixels along 45 degree lines through the
image. A practical way of doing this is to slew each successive row of the
image by one pixel compared to its predecessor. Patches are then selected in 11
overlapping horizontal bands as in Fig. 5. Each patch has eight pixels
vertically (overlapping by four) and 12 horizontally (overlapping six). In the
horizontal direction, a weighted average under a 1/4 Hanning window is formed.
In effect, the resolution in the horizontal (iris circumferential) direction is
reduced by this step. Averaging across the width of the patch helps to reduce
the degrading effects of noise and the use of broad patches makes for easier
iris registration. In the vertical direction (45 degrees from the iris radial),
eight pixels from each patch form a 1D patch vector, which is then windowed using
a similar Hanning window prior to application of the DCT in order to reduce
spectral leakage during the transform. The differences between the DCT
coefficients of adjacent patch vectors are then calculated and a binary code is
generated from their zero crossings. These 8-bit code fragments (code lets) are
the basis of our matching process.

**Matching:**

For
comparing two iris codes, a nearest-neighbor approach is taken, where the
distance between two feature vectors is measured using the product-of-sum (POS)
of individual sub feature Hamming distances (HD). This can be defined as
follows:

Here, we
consider the iris code as a rectangular block of size M x N, M being the number
of bits per sub feature and N the total number of sub features in a feature
vector. Corresponding sub feature bits are XORed and the resultant N-length
vector is summed and normalized by dividing by N. This is done for all M sub feature
bits and the geometric mean of these M sums give the normalized HD lying in the
range of 0 to 1. For a perfect match, where every bit from Feature 1 matches
with every corresponding bit of Feature 2, all M sums are 0 and so is the HD,
while, for a total opposite, where every bit from the first Feature is reversed
in the second, MN/Ns are obtained with a final HD of 1. Since a total bit
reversal is highly unlikely, it is expected that a random pattern difference
should produce an HD of around 0.5. While our previous approach based the HD
calculation on a weighted sum of EXOR-ed bits, the new POS method provides for
better separation by skewing the matching distribution toward 0 and the non matching
one toward 0.5.A side-effect of taking the geometric mean is that an identical
match between any two corresponding sets of N sub feature bits will give an
overall HD of 0 and, thus, a perfect match. Although this might seem radical,
it is highly unlikely that all N bits, 713 in this case, will match identically
for any two iris templates. Even if such a situation were to occur, it is very
likely that both templates originated from the same class. Rotation invariance
is achieved by storing six additional iris codes for three rotations on either
side by horizontal (iris circumferential) shifts of 4, 8, and 12 pixels each
way in the normalized images. During verification, the test iris code is
compared against all seven stored ones and the minimum distance is chosen for
each of the three separately enrolled images. These three minima are then
averaged to give the matching HD.

**RESULT AND DISCUSSION**

Suppose we
want to match iris image no3 as a test image and image is as follows.

Edge
Detection and Matching Process:

Normalization Overlap
patched

Hanning vertical Hanning horozontal

After processing Given Iris Image is matched with 10th Template
(10th Person) stored in database.

**MATLAB Implementation:**

**MAIN GUI:**

**Entry of New User:**

**IRIS Matching:**

**if you want this code then contact us on....**

**Contact**

**Mobile Number: +91-9637253197**

**Whatsup Number: +91-9637253197**

**Email ID: matlabprojects07@gmail.com**

sir can I get the code the for this in matlab

ReplyDelete