Iris Recognition Using Neural Networks

Introduction

There are variable ways of human verification through out the world, as it's of great importance for all organizations, and different centers. Nowadays, the most significant ways of human verification are recognition via DNA, face, fingerprint, signature, speech, and iris.


Among all, among the recent, reliable, and technological methods is iris recognition which is practiced by some organizations today, and its wide usage in the future is of no doubt. Iris is a non identical organism made of colorful muscles including robots with shaped lines. These lines are the main causes of making every one's iris non identical. Even the irises of a pair of eyes of one person are entirely different from one additional. Even in the case of identical twins irises are entirely different. Each iris is specialized by very narrow lines, rakes, and vessels in different folks. The precision of identification via iris is increased by using more and more details. it's been proven that iris patterns are never changed nearly from the time the child is one year old through out all his 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 kind of neural network practiced by the researcher is the Learning Vector Quantization which is a competitive network functional in the field of classification of the patterns. The iris images prepared as a database, is in the form of PNG (portable network graphics) pattern, meanwhile they have to be preprocessed through which the limit of the iris is recognized and their features are extracted. For doing so, edge detection is done by the usage of Canny approach. For more diverse and feature extraction of iris images DCT transform is practiced.

2. Feature Extraction

For increasing the precision of our verification of iris system we ought to extract the features so that they contain the main items of the images for comparison and identification. The extracted features ought to be in a manner that cause the least of flaw in the output of the system and in the ideal condition the output flaw of the system ought to be zero. The useful features which ought to be extracted are obtained through edge detection in the first step and the in next step we use DCT transform.

2.1 Edge Detection

The first step locates the iris outer limit, i.e. border between the iris and the sclera. This is done by performing edge detection on the gray scale iris image. In this work, the edges of the irises are detected using the "Canny method" which finds edges by finding local maxima of the gradient. The gradient is calculated using the derivative of a Gaussian filter. The method applies two thresholds, to detect strong and weak edges, and includes the weak edges in the output only if they're connected to strong edges. This method is robust to additive noise, and able to detect "true" weak edges.

Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they're normally affected by one or a lot of of these effects: focal blur caused by a finite depth-of-field and finite point spread function, penumbral blur caused by shadows created by light sources of non-zero radius, shading at a smooth object edge, and local specularities or inter reflections in the vicinity of object edges.

2.1.1 Canny method

The Canny edge detection algorithm is known to a lot of as the optimal edge detector. Canny's intentions were to enhance the a lot of edge detectors already out at the time he started 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 paper, he followed a list of criteria to improve current methods of edge detection. The first and most clear is low error rate. it's significant that edges existing in images ought to not be missed and that there be NO responses to non-edges. The second criterion is that the edge points be well localized. In other words, the distance between the edge pixels as found by the detector and the actual edge is to be at a minimum. A third criterion is to have only one response to a single edge. This was implemented because the first 2 were not substantial enough to entirely eliminate the possibility of multiple responses to an edge.

The Canny operator works in a multi-stage process. First of all the image is smoothed by Gaussian convolution. Then a simple 2-D first derivative operator (fairly like the Roberts Cross) is applied to the smoothed image to highlight regions of the image with significant spatial derivatives. Edges give rise to ridges in the gradient magnitude image. The algorithm then tracks along the top of these ridges and sets to zero all pixels that are not in reality on the ridge top so as to give a thin line in the output, a process called non-maximal suppression. The tracking process exhibits hysteresis controlled by two thresholds: T1 and T2, with T1 > T2. Tracking can only begin at a point on a ridge higher than T1. Tracking then continues in both directions out from that point until the height of the ridge falls below T2. This hysteresis helps to make sure that noisy edges are not broken up into multiple edge fragments.

2.2 Discrete Cosine Transform

Like any Fourier-related transform, discrete cosine transforms (DCTs) express a function or a signal in terms of a sum of sinusoids with different frequencies and amplitudes. Like the discrete Fourier transform (DFT), a DCT operates on a function at a finite number of discrete data points. The clear distinction between a DCT and a DFT is that the former applies only cosine functions, while the latter applies both cosines and sinusoids (in the form of complex exponentials). even so, this visible difference is merely a consequence of a deeper distinction: a DCT implies different limit conditions than the DFT or other related transforms.

The Fourier-related transforms that operate on a function over a finite domain, 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 you write a function f(x) as a sum of sinusoids, you are able to evaluate that sum at any x, even for x where the original f(x) was not specified. The DFT, like the Fourier series, implies a periodic extension of the original function. A DCT, like a cosine transform, implies an even extension of the original function.

A discrete cosine transform (DCT) expresses a sequence of finitely a lot of data points in terms of a sum of cosine functions oscillating at different frequencies. DCTs are significant to numerous applications in science and engineering, from lossy compression of audio and images (where small high-frequency components can be discarded), to spectral methods for the numerical solution of partial differential equations. The use of cosine instead of sine functions is critical in these applications: for compression, it turns out that cosine functions are a lot more efficient (as explained below, fewer are needed to approximate a typical signal), whereas for differential equations the cosines express a particular choice of limit conditions.

In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using 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, of which four are common.

The most common variant of discrete cosine transform is the type-II DCT, which is often known as simply "the DCT"; its inverse, the type-III DCT, is correspondingly often known as simply "the inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform (DST), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transform (MDCT), which is based on a DCT of overlapping data.

The DCT, and in particular the DCT-II, is often used in signal and image processing, especially for lossy data compression, because it has a strong "energy compaction" property. Most of the signal information tends to be concentrated in a couple of low-frequency components of the DCT.

3. Neural Network

In this work one Neural Network structure is used, which is Learning Vector Quantization Neural Network. A brief overview of this network is given below.

3.1 Learning Vector Quantization

Learning Vector Quantisation (LVQ) is a supervised version of vector quantisation, similar to Selforganising Maps (SOM) based on work of Linde et al, Gray and Kohonen. it is able to be applied to pattern recognition, multi-class classification and data compression tasks, e.g. speech recognition, image processing or customer classification. As supervised method, LVQ applies known target output classifications for each input pattern of the form.

LVQ algorithms don't approximate density functions of class samples like Vector Quantisation or Probabilistic Neural Networks do, but directly define class boundaries based on prototypes, a nearest-neighbour rule and a winner-takes-it-all paradigm. The main theme is to cover the input space of samples with 'codebook vectors' (CVs), each representing a region labelled with a class. A CV can be seen as a prototype of a class member, localized in the centre of a class or decision region in the input space. A class can be represented by an arbitrarily number of CVs, but one CV represents one class only.

In terms of neural networks a LVQ is a feedforward net with one hidden layer of neurons, fully connected with the input layer. A CV can be seen as a hidden neuron ('Kohonen neuron') or a weight vector of the weights between all input neurons and the regarded Kohonen neuron respectively.

Learning means modifying the weights in accordance with adapting rules and, so, changing the position of a CV in the input space. Since class boundaries are built piecewise-linearly as segments of the mid-planes between CVs of neighbouring classes, the class boundaries are adjusted during the learning process. The tessellation induced by the set of CVs is optimal if all data inside one cell indeed belong to the same class. Classification after learning is based on a presented sample's vicinity to the CVs: the classifier assigns the same class label to all samples that fall into the same tessellation - the label of the cell