Real-Time Iris Identification

The present invention is directed to a method and apparatus for performing the one-dimensional (1D) moving average filter based on real-time which is based on a suitable setting of a sliding window of a limited size and the calculation of its mean value. We have obtained the following two theorems:
Theorem 1. If the length of the sliding window is s and the x-th position is not-k where:
1xe2x89xa6kxe2x89xa6s
then the filtering value between x and k/s+1 for the mean of this window is:
{overscore (I)}x=1/s x+1/s x+1/s x+1/s x+1/s x+1/s x+1/s x+…
Theorem 2. The filtering value of the mean of the sliding window is thus obtained:
{overscore (I)}x=A(k) if xxe2x89xa6k
{overscore (I)}x={overscore (I)}(kxe2x88x921)/s+A(kxe2x88x921) if xxe2x89xa6kxe2x88x921
{overscore (I)}x=A(0)+A(1) if
In the above two theorems, we have used the following notation:
{overscore (I)}x= a(x)
A(k)= a(k)s
where
s=magnitude of the sliding window
A(k)= the mean of the sliding window with the position k and a is the mean of the sliding window with the position k-1
A(0)= the mean of the sliding window with the position 0
If the length of the sliding window is S, we can get A(k) for k=0, 1, 2, 3… S/3, from A(k-1). The method for calculation of A(k) is described in detail in the appended list of references.
Finally, the Iris Modeling Method of the present invention can be summarized as:
The method for 1D moving average filtering comprises the steps of:
Mean filtering the image with fixed window size;
Calculating the mean value of the filtered image according to the two theorems

The Real-Time Iris Identification Torrent Download technique using moving average filter is based on the following
1. Create a 1D moving average filter. The purpose is to reduce the effects of noise.
2. Pass the filtered image through a pre-trained neural network (NN). The NN implements linear classifiers.
3. Compare the filtered neural network output with a threshold to output a decision.
1D Moving Average Filter:
We can create a moving average filter by calculating the average of the pixels around a certain pixel. Suppose that we have an m-pixels image with a size of m×n. The average of the pixels in the range (c×d), (c×d+1), (c×d+2),…, (c×d+m-1), where c and d are integers, is calculated as follows:

c

(1)

d

d

d

d+1

d

d+2

2

3

Averaging can be performed in three ways as described below:

1. Horizontal: Averaging in a vertical direction. The distance along a horizontal line is shown in Fig. 1(a).

Fig. 1(a): Horizontal averaging.

2. Vertical: Averaging in a horizontal direction. The distance along a horizontal line is shown in Fig. 1(b).

Fig. 1(b): Vertical averaging.

3. Middle: Averaging in the center of the image. The distance along a horizontal line is shown in Fig. 1(c).

Example 1:

Create a 2-pixel moving average filter using horizontal averaging.
Input:

Output:

c

d

1

1

2

2

Averaging can be performed in two ways:

1. Horizontal averaging: Horizontal averaging is used to minimize the errors between the pixels at the top and bottom of the image, and between the pixels at the left and right of the image.

2. Vertical averaging: Vertical averaging is used to minimize the errors between the pixels at the left and right of the image, and between the pixels at the top and bottom of the image.

Example 2:

Create a 3-pixel moving average filter using horizontal averaging.
Input:

Output:

c

d

1
09e8f5149f

In this approach we perform the calculations for the mean of an 11-point 1D moving average filter in a recursive way and compare the results of the 1D moving average filter to a threshold. If the output is greater than the threshold the method assumes that the input sequence is an iris image and vice versa.
The 1D moving average filter can be mathematically expressed as:
h ⁡ ( n ) = ∑ i = 0 n ⁢ x ⁡ ( i –

Real Time Iris Recognition also known as iris recognition is a technology that requires a user to hold their eyes still for the duration of a scan or take a series of stills. Iris recognition is an emerging biometric technique used to identify individuals for purposes ranging from access control to credit card validation. Iris detection, image capture and analysis are all done in real time so that the user does not need to be physically present at the time of image capture. This is ideal for a lot of scenarios such as border security, government identification, warehouse security, access control in locations such as banks, airports, warehouses, etc.

The iris is a colored ring of tissue located around the pupil of the eye that is able to change in size and shape. This project uses a 1D moving average filter to compress the bitmap image and then locate the pupil. A bitmap image of the iris is grabbed in the center of the iris using a simple C++ program. Then a 1D moving average filter is applied to the bitmap image and the pupil of the iris is located.

The iris is a colored ring of tissue located around the pupil of the eye that is able to change in size and shape. This project uses a 1D moving average filter to compress the bitmap image and then locate the pupil. A bitmap image of the iris is grabbed in the center of the iris using a simple C++ program. Then a 1D moving average filter is applied to the bitmap image and the pupil of the iris is located.

How to build this project by using C++?

1. Download this project and extract the files which are provided in the ZIP.
2. Find the libusb-1.0.dll file and copy it to a new folder.
3. Now open C:\wintypes.h file and add Line no. 192 as following. (no #include

Line #192:

#include

4. Now open C:\kernel.h file and add Line no. 39 as following. (no #include

Line #39:

#include

5. Now open

-Intel® Core™ i5-2400 CPU or better
-8GB of system memory (RAM)
-1GB of video memory
-9.7GB of available hard disk space
-Microsoft® Windows® 7 or later
-DirectX® 11.1
-At least a resolution of 1920 x 1080
Copyright © 1999-2020 Electronic Arts Inc. and its licensors. All Rights Reserved. Electronic Arts and the EA logo are trademarks of Electronic Arts Inc.
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