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Iris Recognition Using Neural Networks
There are multiple ways of manual verification all over the world because it is very important for all organizations and different centers. Today, the most important form of human authentication is identification through DNA, face, fingerprint, signature, voice and iris.
Among them, iris recognition is an up-to-date, reliable, and technological method that is being practiced by some organizations today, and its widespread use in the future is without doubt. Iris is a different organism, made of colorful muscles, including robots with shaped lines. These lines are the main reason why everyone’s iris is different. Even the irises of a person’s pair of eyes are completely different. Even in the case of identical twins, the irises are completely different. Each iris is specialized with very narrow lines, rakes, and vessels in different people. Improves the accuracy of iris recognition by using more and more detail. It turns out that iris patterns almost never change from the time a child is one year old, and throughout life.
Over the past few years, there has been considerable interest in the development of neural network-based pattern recognition systems because of their ability to classify data. The type of neural network the researchers practice is learning vector quantization, a network function that is competitive in the field of pattern classification. Iris images are prepared as a database, in the form of PNG (Portable Network Graphics) patterns, while they must be preprocessed by identifying the boundaries of the iris and extracting its features. For this, edge detection is done by using the Canny method. In order to make the iris image more diverse and feature extraction, DCT transformation is adopted.
2. Feature extraction
To improve the accuracy of iris system verification, we should extract features such that they contain the main items of images for comparison and identification. The extracted features should appear in such a way that they cause the least amount of defects in the output of the system, and ideally the system should have zero output defects. The useful features that should be extracted are obtained by edge detection in the first step, and we use DCT transformation in the next step.
2.1 Edge detection
The first step locates the outer boundary of the iris, that is, the border between the iris and the sclera. This is done by performing edge detection on the grayscale iris image. In this work, the edge of the iris is detected using the “Canny method”, which finds the edge by finding a local maximum of the gradient. Computes the gradient using the derivative of a Gaussian filter. The method uses two thresholds to detect strong and weak edges, and only includes weak edges in the output if they are connected to strong edges. The method is robust to additive noise and able to detect “true” weak edges.
Although some literature considers the detection of ideal stepped edges, the edges obtained from natural images are often not ideal stepped edges at all. Instead, they are usually affected by one or more of the following effects: focus blur caused by finite depth of field and finite point spread function, penumbral blur caused by shadows produced by non-zero radius light sources, shadow edges of smooth objects and object edges Nearby local specular reflections or mutual reflections.
2.1.1 Canny method
The Canny edge detection algorithm is known by many as the best edge detector. Canny’s intention was to enhance the many edge detectors that were already available when he started his work. He achieved his goal very successfully and his ideas and approach can be found in his paper “A Computational Approach to Edge Detection”. In his thesis, he followed a set of criteria to improve current edge detection methods. First and most obvious is the low error rate. It is important that edges present in the image should not be missed and there is no response to non-edges. The second criterion is that the edge points are well positioned. In other words, the distance between the edge pixels found by the detector and the actual edge should be minimized. The third criterion is that there is only one response to a single edge. This is because the first 2 are not enough to completely eliminate the possibility of multiple responses to edges.
Canny operators work in a multi-stage process. First, the image is smoothed by Gaussian convolution. A simple 2D first derivative operator (sort of like a Roberts cross) is then applied to the smoothed image to highlight image regions with significant spatial derivatives. Edges produce ridges in the gradient-magnitude image. The algorithm then traces along the tops of these ridges and sets all pixels that are not actually on top of the ridges to zero to give a thin line in the output, a process called non-maximum suppression. The tracking process exhibits hysteresis controlled by two thresholds: T1 and T2, where T1 > T2. Tracking can only be started from points on the ridge higher than T1. Tracking is then continued in both directions from this point until the height of the ridge is below T2. This lag helps ensure that noisy edges are not broken up into multiple edge fragments.
2.2 Discrete Cosine Transform
Like any Fourier-related transform, the discrete cosine transform (DCT) represents a function or signal in terms of a sum of sine waves with different frequencies and amplitudes. Like the Discrete Fourier Transform (DFT), the DCT operates on functions on a finite number of discrete data points. The obvious difference between DCT and DFT is that the former uses only the cosine function, while the latter uses both cosine and sine curves (in the form of complex exponentials). However, this visible difference is only the result of a deeper distinction: DCT implies different boundary conditions than DFT or other related transforms.
A Fourier-related transform that operates on a function over a finite field, such as the DFT or DCT or a Fourier series, can be thought of as implicitly defining an extension of that function outside the domain. That is, once a function f(x) is written as a sum of sinusoids, the sum can be computed at any x, even for x for which the original f(x) is not specified. Like Fourier series, DFT implies a periodic extension of the original function. Like the cosine transform, DCT implies a uniform extension of the original function.
The discrete cosine transform (DCT) represents a sequence of finitely many data points in terms of the sum of cosine functions oscillating at different frequencies. DCTs are important for numerous applications in science and engineering, from lossy compression of audio and images (which can discard small high-frequency components) to spectral methods for the numerical solution of partial differential equations. It is crucial to use the cosine function instead of the sine function in these applications: for compression, the cosine function has been shown to be more efficient (less is needed to approximate typical signals, as explained below), while for differential equations, the cosine represents boundary conditions special choice.
In particular, the DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using only real numbers. The DCT is equivalent to a DFT of about twice the length, operating on even symmetric real data (since the Fourier transform of a real even function is a real even function), 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 Type III DCT is correspondingly often referred to simply as “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, especially DCT-II, is often used in signal and image processing, especially lossy data compression, because of its strong “energy compression” properties. Most signal information tends to be concentrated in a few low-frequency components of the DCT.
3. Neural network
In this work, a neural network structure, the learned vector quantization neural network, is used. A brief overview of the network is given below.
3.1 Learning Vector Quantization
Learning vector quantization (LVQ) is a supervised version of vector quantization, similar to self-organizing maps (SOMs) based on the work of Linde et al., Gray and Kohonen. It can be applied to pattern recognition, multiclass classification, and data compression tasks such as speech recognition, image processing, or customer classification. As a supervised method, LVQ uses a known target output classification for each input pattern of the form.
The LVQ algorithm does not approximate the density function of class samples like vector quantization or probabilistic neural network, but directly defines the class boundary based on the prototype, the nearest neighbor rule and the winner-take-all paradigm. The main idea is to cover the input space of samples with “codebook vectors” (CVs), each representing a class-labeled region. CVs can be viewed as prototypes of class membership, centered in the class or decision region in the input space. A class can be represented by any number of CVs, but a CV represents only one class.
In terms of neural networks, LVQ is a feed-forward network with a hidden layer of neurons fully connected to the input layer. CV can be seen as a weight vector of weights between a hidden neuron (‘Kohonen neuron’) or all input neurons and the considered Kohonen neuron.
Learning means modifying the weights according to an adaptive rule, thus changing the position of the CV in the input space. Since the class boundaries are constructed piecewise linearly as segments of the midplane between adjacent class CVs, the class boundaries are adjusted during the learning process. Tessellation induced by a set of CVs is optimal if all the data within a cell really belongs to the same class.The learned classification is based on the proximity of the presented sample to the CV: the classifier assigns the same class label to all samples belonging to the same mosaic – the label of the cell
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