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3.3 Iris Scan Biometric
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El-Bakry [5] further investigates the number of computation steps required by the neural nets on varying image sizes. It is found that as the size of the image increases, the number of computations does not increase linearly; that is, the number of computations for a 100 100 image is far greater as compared to a 50 50 image. To overcome this disadvantage, the author proposes decomposing the image into subimages and then testing these subimages separately. Further improvement can be achieved by using parallel processors on these subimages. Thus the speedup ratio increases and the running time decreases. El-Bakry [5] claims that the simulation results show that the proposed algorithm is an ef cient method for nding locations of irises when the size of the iris is unknown. Also rotated, scaled, occluded, noised, and mirrored irises are detected correctly at different illumination levels. The proposed method is also suitable for detecting the presence or absence of any other object in an image.
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3.3.2 Iris Recognition by a Rotation Spreading Neural Network
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Murakami et al. [6, 7] present a rotation spreading neural network (R-SAN net). This neural net can recognize the orientation of an object irrespective of its shape and its shape irrespective of its orientation. Thus, it is suitable for shape and orientation recognition of a concentric circular pattern because it uses polar conversion. The authors propose to experiment with R-SAN net to simultaneously recognize the orientation and shape of the iris images of people who have had their irises registered. The input pattern of 300 300 pixels is transformed to polar coordinates. This transformed pattern is input into the spreading layer, and the spread pattern is obtained. In the learning stage, the memory matrix of the orientation and shape are obtained by generalized inverse learning. The number of learning patterns is the product of the number of learning shapes and the number of learning orientations. The spread image corresponding to the respective spreading weight is obtained by multiplication of the transformed image with the spreading weight, which is the periodic Gaussian curve function predetermined at equal intervals in various directions. The spread image is summed in each direction and combined to produce the spread pattern vector. A population vector is created to indicate the orientation of the object. In the recollection stage, the output of orientation recognition neurons is obtained by multiplying the spread pattern and orientation memory matrix, and the output of shape recognition neurons is obtained by the spread pattern and shape memory matrix. The recognition experiments were performed in three sessions; the number of subjects was 3, 5, and 10, respectively. For each subject, six iris patterns oriented at six orientations were generated from one iris image, and these were used for learning the R-SAN net. The pupil position was detected using a partial eye template for the eye image taken by a compact close-up camera. Murakami et al. [6, 7] conclude that iris recognition is possible in any arbitrary orientation without depending on zoom of the camera. In an experiment with unlearned test iris patterns, the R-SAN net combined
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A Comparative Survey on Biometric Identity Authentication Techniques
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with minimum distance as the shape recognition criterion rejected the unregistered iris patterns.
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FINGERPRINT BIOMETRIC
Fingerprint identi cation is the process of comparing given and known skin ridge impressions from ngers to determine if the impressions are from the same nger, thus identifying or verifying the owner of the given ngerprint. In this section we review representative works describing ngerprint biometric, which is by far the most commonly used biometric.
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3.4.1 Fingerprints Classification Using Artificial Neural Nets: A Combined Structural and Statistical Approach
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Nagaty [8] describes a ngerprint classi cation algorithm using multilayer arti cial neural networks (ANN). A method for coarse level classi cation of the ngerprint images by combining both the structural features and statistical measure of ngerprint patterns is introduced. Fingerprints are classi ed into six categories: arches, tented arches, left loops, right loops, whorls, and twin loops. After preprocessing and normalization of the ngerprint image, its block directional image is calculated and is used to extract both statistical and structural features required for classi cation. The classi cation algorithm consists of four major steps: 1. Compute the block directional image. 2. Extract the structural features of the pattern. 3. Compute the statistical measures of the pattern. 4. Design a multilayer ANN composed of six subnet works for the six ngerprint classes and use a multivariate input vector, which is a combination of the structural features and statistical measures. In the rst step of calculating the block directional image, the ngerprint image is divided into a number of squares and the local orientation is computed in these squares. Later on, the spurious directions in the block directional image are smoothened. A set of horizontal and vertical operators are used iteratively for the smoothening purpose. These block directions of the directional image are converted into binary blocks so that the resultant curves can be traced using a line tracing algorithm. The transformed block directional image is composed of ones, which act as a link between various curves of the pattern. The pattern is scanned from left to right and from top to bottom by using a set of feature masks. Each mask is assigned a symbol, and each connected curve consists of a string of symbols that represent the curve without any loss of information. The alphabetic symbols used to represent the curve strings are then transformed to map an input vector to the ANN. Every symbol is transformed to a binary string.
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