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This paper presents a biometric technique for identification of a person using the iris image. The iris is first segmented from the acquired image of an eye using an edge detection algorithm. The disk shaped area of the iris is transformed into a rectangular form. Described moments are extracted from the grayscale image which yields a feature vector containing scale, rotation, and translation invariant moments. Images are clustered using the k-means algorithm and centroids for each cluster are computed. An arbitrary image is assumed to belong to the cluster whose centroid is the nearest to the feature vector in terms of Euclidean distance computed. The described model exhibits an accuracy of 98.5%.

Identification of individuals has been an important need over the ages. Conventionally identification documents like an identity card, passport, or driving license have been utilized for the purpose. Such identification methods have been evaded several times by use of forged documents. In the digital world a login and a password or a PIN code is used for identification. Besides shoulder surfing and sniffing several other techniques have evolved which are used to crack such codes and breach security. Undoubtedly a robust identification technique is essential for a safe and well supervised environment. This situation thrives the need of an identification technique using some biological inimitable features of a person. Numerous biological human features are peculiar and unique such as fingerprints, suture patterns, iris patterns, gait, and ear shapes. The patterns found in these structures are unique for every human; hence they can be used as an identification tool. In the recent past, use of iris image of a person for his identification has gained popularity. The radial and longitudinal muscles in the iris of an eye are responsible for the constrictions and dilation of the pupil. The pupil changes its size depending upon the light intensity the eye is exposed to. The muscles of iris form the texture of the iris while the presence or absence of a pigment forms the color of the iris. The color of the iris is genetically dependent, whereas the texture is not. The texture of iris forms random unique patterns for each human. A close observation of an iris may reveal pustules, rings, stripes, and undulations forming a unique pattern.

In the recent past, researchers have developed several mechanisms for matching the pattern that lies within the iris. In [

Most of the techniques based on feature extraction are designed for image of a certain fixed resolution. They fail to provide the desired result for the same images with different resolution. This characteristic implies that the model is not scale invariant. Techniques making use of NN incorporate a time taking training procedure. At times this training process may prove to be tricky rendering the model unable to yield quick results. On the other hand, some techniques that make use of certain filters may produce undesired results if the image is rotated which implies that such models are not rotation invariant. In this underlying paper a scale and rotation invariant technique for the same purpose is described. The proposed technique requires little training after which results are produced instantly. It is based on the use of image moments. Moments are properties that describe the characteristics of a certain distribution of data. Image moments (namely, Hu moments) are a quantitative measure of the shape of distribution formed by data collected as image pixel intensities and their locations [

In the proposed work the iris is segmented from an eye image. Image moments are computed from the segmented grayscale image. Classification of an iris is performed by the k-means algorithm. The composition of the paper is as described here. Section

Initially the image of an eye is acquired by a device called iriscope specifically designed for eye image acquisition at a high resolution. A large database of such images is collected having several classes. The iris within the image is segmented using an accurate and sufficiently fast technique. The iris image is of radial nature, rather than rectangular, which makes it unsuitable to be processed by any mathematical or statistical model of linear nature. There are two approaches to resolve this problem. The first approach is to adapt a model capable of processing data in its inherent radial form. Other approaches require transformation of the radial data into multidimensional linear form such that the information pertaining to iris texture is retained. In this piece of work the latter approach is adopted.

The information within the texture of the rectangular image may be used to form a probability density function. The image moments quantify the characteristics of this distribution. Using these raw moments translation, scale and rotation invariant moments are computed. Accumulated, these moments describe the characteristics of the pattern of the iris. This forms a feature vector which is later used for classification of iris images.

Each image in the database contains the iris pattern which is of interest; the rest of the image is of no use and therefore is not processed. The iris is extracted from the image using the segmentation process described in [

The figure depicts iris image after edge detection making disk shaped edges apparent.

The figure shows an iris image after edge detection

The figure shows the disk shaped characteristics of iris. Note the circles overlapping the inner and outer circular edges

Transforming the radial iris into rectangular form.

Putting the values of

An arbitrary Cartesian point

The figure illustrates iris images before and after radial to rectangular transformation.

The computations required to compute each point within the disk shaped structures are reduced by exploiting the symmetric properties of a circle. A circular shape exhibits eight-way symmetry [

The

The determination of points along a line is further optimized by the use of the midpoint method [

Let

A perceptive action performed on intricate structures needs to quantify its attributes. The state of any structure is quantifiable into data. Diversification of this data represents interaction or changes in the state. All such quantification methods generate finite data. Data by itself is insignificant, but the information implanted within the data is useful. Information is either extracted directly from the data itself or from the patterns formed by the arrangement of data. Researchers have devised various models for extracting information from data embedded in an image. Applications based on such models do not add to the contents of data rather they find hidden data patterns in order to extract interesting and useful information. A probability density can be formed for any data set. The parameters of the probability density function inform us about the general manner in which data is distributed. Moments are the characteristics of the probability density function which are based on the kurtosis and skewedness of the probability density function. Image moments describe the properties of a distribution formed using the pixel data of the image along its axes. The moments are typically chosen to depict a certain interesting property of the image. Such moment proves beneficial in extracting and summarizing the properties of the image in order to produce useful results. Properties of an image such as centroid, area, and orientation are quantified by this process. Another dividend of image moments is that they bring together the local and global geometric details of a grayscale image [

An image in the real world is modeled using a Cartesian distribution function

In [

Once the centroid is determined, it is used to compute the centralized moments. In [

These moments are further made scale invariant as explained in [

The second order central moments contain information about the orientation of the image. Using these moments a covariance matrix is derived. Let

The major and minor axes of the image intensity correlate with the eigenvectors of the given covariance matrix. The orientation of the image is described by the eigenvector of the highest eigenvalue. In [

Previously we have discussed translation and scale invariant moments. In [

By now the iris image has been segmented and transformed into a rectangular canvas. All described moments are applied and a feature vector is extracted, namely,

The k-means Algorithm has two major steps, namely, the assignment step and the update step. The mean is used to assign each observation to a cluster in the assignment step. An observation is assigned a cluster whose mean makes the closest match. Formally this step generated the set

Also an observation

The CASIA database containing thousands of images belonging to hundreds of different people is used to gather test results. Nearly one-fourth of the iris images from each class are retained as test case while the rest are used for training. The distorted images within the database are rejected. Iris portion of the image is marked out using the segmentation algorithm and is later transformed into a rectangular canvas. Further the grey scale rectangular canvas of iris is used to compute image moment vector. This vector contains information which is translation, scale, and rotation invariant and provides orientation information. Using the k-means algorithm each image is assigned to a cluster. The k-means algorithm is iterated until convergence is achieved and the centroid of each cluster is determined. Once the system is fully trained it is ready to accept an arbitrary input and provide a match. The model responds with the correlation of an arbitrary image moments vector to a cluster, if the image belongs to a known class. In Figure

Each of (a) and (b) shows different clusters. Notice that all the clusters are linearly separable and can be distinguished by their Euclidean distance from the Centroid.

The figure shows the confusion matrix for some arbitrary classes, while the accuracy of the model for these classes is 99.0%.

Furthermore a number of experiments were carried out to determine the accuracy and efficiency of the proposed model in comparison with other competitive models. In [

The figure illustrates the receiver operating characteristics distributions for different competitive techniques including the proposed one.

Through analysis of data obtained after moments extraction a number of conclusions are inferred. Images of a certain iris differing in orientation yielded varying eigenvalues and eccentricity. However, a change in orientation of an image barely affects the values of rotation invariant moments while raw and scale invariant moments are affected. Change in orientation of an image affects the Euclidean distance of the moment vectors from the centroid. Despite this there still remains a great probability of the image to be classified correctly because of coherence in scale invariant moments. Although the model exhibits scale and rotation invariant attributes but some impairment is offered by luminosity of the image. Two arbitrary images of the same objects yield comparable moments if the luminosity is the same but they may yield differing moments in case luminosity is altered. In the underlying research work it is assumed that the luminosity level will be the same for all the images as each image is obtained by an iriscope working in similar conditions. The model provides resilience towards variation of scale and rotation as compared to other techniques which requires coherence of phase and size. The model can be further improved by incorporation of a technique that will process each image to provide uniform luminosity. Furthermore, the ROC distribution obtained (shown in Figure

The authors declare that there is no conflict of interests regarding the publication of this paper.