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Iris Recognition Using Neural Networks
There are different ways of human verification all over the world, because it is of great importance for all organizations and different centers. Today, the most important ways of human verification are recognition through DNA, face, fingerprint, signature, speech and iris.
Among other things, one of the newer, reliable and technological methods is iris recognition, which is practiced today by some organizations, and there is no doubt about its wide application in the future. Iris is a non-identical organism composed of colorful muscles including robots with shaped lines. These lines are the main reason why everyone’s iris is not identical. Even the irises of a pair of eyes of one person are completely different from each other. Even in the case of identical twins, the irises are completely different. Each iris is specialized with very narrow lines, rakes and veins in different people. The accuracy of iris identification increases with the use of more and more details. It has been proven that the iris never changes from the child’s first year of life until the end of life.
Over the past few years, there has been considerable interest in the development of pattern recognition systems based on neural networks, due to their ability to classify data. The type of neural network practiced by the researcher is learning vector quantization which is a competitive network function in the field of pattern classification. Images of the iris prepared as a database are in the form of a PNG (portable network graphics) sample, while they must be pre-processed through which the borders of the iris are recognized and their features are distinguished. To do this, edge detection is done using the Canny approach. For more diverse and meaningful extraction of iris images, DCT transformation is practiced.
2. Feature extraction
To increase the precision of our iris system verification, we should extract the features to contain the main items of the images for comparison and identification. The extracted features should be such that they cause the least error in the system output, and ideally the system output error should be zero. Useful features to be extracted are obtained by edge detection in the first step, and in the next step we use the DCT transformation.
2.1 Edge detection
The first step locates the outer border of the iris, i.e. the border between the iris and the sclera. This is achieved by performing edge detection on the grayscale iris image. In this work, iris edges are detected using the “Canny method” which finds edges by finding local gradient maxima. The gradient is calculated using the derivative of the Gaussian filter. The method uses two thresholds to detect strong and weak edges and includes weak edges in the output only if they are associated with strong edges. This method is robust to additional noise and can detect “real” weak edges.
Although some literature considers the detection of ideal step edges, the edges obtained from natural images are usually not ideal step edges at all. Instead, they are typically affected by one or more of these effects: focal blur caused by the finite depth of field and finite point spread function, penumbra blur caused by shadows cast by light sources of non-zero radius, smooth object edge shading, and local specularity or interreflections near edges of the object.
2.1.1 Canny method
Canny’s edge detection algorithm is known to many as the optimal edge detector. Canny’s intentions were to improve upon the many edge detectors that were already available at the time he began his work. He was very successful in achieving his goal, and his ideas and methods can be found in his paper “A Computational Approach to Edge Detection”. In his work, he followed a list of criteria for improving current edge detection methods. The first and most obvious is the low error rate. It is important that the edges that exist in the images are not missed and that there is NO response to non-edges. Another criterion is that the edge points are well localized. In other words, the distance between the edge pixels determined by the detector and the actual edge must be minimal. The third criterion is to have only one answer to one edge. This was implemented because the first 2 were not significant enough to completely eliminate the possibility of multiple edge responses.
The canny operator works in a multiphase process. First of all, the image is smoothed by Gaussian convolution. Then a simple 2-D first-derivative operator (something like a Roberts cross) is applied to the smoothed image to highlight areas of the image with important spatial derivatives. Edges create ridges in the gradient size image. The algorithm then traces along the top of those ridges and sets to zero any pixels that aren’t actually on top of the ridge to produce a thin line in the output, a process known as non-maximal suppression. The tracking process shows hysteresis controlled by two thresholds: T1 and T2, with T1 > T2. Tracking can only start at a point on the ridge higher than T1. Tracking then continues in both directions from that point until the crest height falls below T2. This hysteresis helps ensure that edges with noise are not broken into multiple edge fragments.
2.2 Discrete cosine transformation
Like any Fourier transform, discrete cosine transforms (DCTs) express a function or signal in terms of the sum of sinusoids with different frequencies and amplitudes. Like the Discrete Fourier Transform (DFT), DCT operates on a function on a finite number of discrete data points. The obvious difference between DCT and DFT is that the former uses only cosine functions, while the latter uses both cosines and sinusoids (in the form of complex exponentials). However, this apparent difference is only a consequence of a deeper difference: DCT implies different boundary conditions than DFT or other related transformations.
Fourier transforms acting on a function in a finite domain, such as a DFT or DCT or Fourier series, can be thought of as implicitly defining the extension of that function outside the domain. That is, once you write the function f(x) as a sum of sinusoids, you can calculate that sum at any x, even for x where the original f(x) is not specified. The DFT, like the Fourier series, implies a periodic expansion of the original function. DCT, like the cosine transform, implies a uniform expansion of the original function.
The discrete cosine transform (DCT) expresses a sequence of a finite number of data points in terms of the sum of cosine functions oscillating at different frequencies. DCTs are important for many applications in science and engineering, from lossy audio and image compression (where small high-frequency components can be rejected), to spectral methods for numerically solving partial differential equations. Using cosine instead of sine functions is crucial in these applications: for compression, cosine functions turn out to be much more efficient (as explained below, fewer are needed to approximate a typical signal), while for differential equations cosines express a special choice of boundary conditions.
Specifically, the DCT is a Fourier transform similar to the Discrete Fourier Transform (DFT), but uses only real numbers. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), where in some variants the input and output data are shifted by half a sample. There are eight standard DCT variants, four of which are common.
The most common variant of the discrete cosine transform is the type-II DCT, often referred to simply as “DCT”; its inverse, the type-III DCT, is appropriately often called simply “inverse DCT” or “IDCT”. Two related transforms are the Discrete Sine Transform (DST), which is equivalent to the DFT of real and odd functions, and the Modified Discrete Cosine Transform (MDCT), which is based on the DCT of overlapping data.
DCT, and especially DCT-II, is often used in signal and image processing, especially for lossy data compression, because it has a strong “energy compression” property. Most signal information tends to be concentrated in a few low-frequency components of the DCT.
3. Neural network
In this work, one neural network structure is used, which is a vector quantization learning neural network. Below is a brief overview of this network.
3.1 Learning vector quantization
Learning vector quantization (LVQ) is a supervised version of vector quantization, similar to self-organizing maps (SOM) based on the work of Linde and co-workers, Gray and Kohonen. It can be applied to pattern recognition, multi-class classification and data compression tasks, e.g. speech recognition, image processing or customer classification. As a supervised method, LVQ uses known target output classifications for each input pattern pattern.
LVQ algorithms do not approximate class sample density functions as vector quantization or probabilistic neural networks do, but directly define class boundaries based on prototypes, nearest neighbor rules, and winner-take-all paradigms. The main idea is to cover the input sample space with ‘codebook vectors’ (CVs), each representing a region labeled by a class. A resume can be viewed as a prototype of a class member, localized in the center of a class or decision region in the input space. A class can be represented by any number of resumes, but one resume represents only one class.
In terms of neural networks, LVQ is a feedforward network with one hidden layer of neurons, fully connected to the input layer. CV can be seen as a hidden neuron (‘Kohonen neuron’) or a weighted vector of weights between all input neurons and the considered Kohonen neuron.
Learning means modifying the weights according to the adaptation rules and, therefore, changing the position of the CV in the input space. Since the class boundaries are constructed piecewise-linearly as midplane segments between the CVs of adjacent classes, the class boundaries adjust during the learning process. A tessellation induced by a set of CVs is optimal if all data within a single cell indeed belong to the same class. Post-learning classification is based on the proximity of the presented sample to CVs: the classifier assigns the same class label to all samples that fall into the same tessellation – cell label
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