Experimental Approach On Thresholding Using Reverse Biorthogonal Wavelet Decomposition For Eye Image

This study focus on compression in wavelet decomposition for security in biometric data. The objectives of this research are two folds: a) to investigate whether compressed human eye image differ with the original eye and b) to obtain the compression ratio values using proposed methods. The experiments have been conducted to explore the application of sparsity-norm balance and sparsity-norm balance square root techniques in wavelet decomposition. The eye image with [320x280] dimension is used through the wavelet 2D tool of Matlab. The results showed that, the percentage of coefficients before compression energy was 99.65% and number of zeros were 97.99%. However, the percentage of energy was 99.97%, increased while the number of zeros was same after compression. Based on our findings, the impact of the compression produces different ratio and with minimal lost after the compression. The future work should imply in artificial intelligent area for protecting biometric data

Feature Extraction From Epigenetic Traits Using Edge Detection In Iris Recognition System

Iris recognition is the most accurate biometric identification system on hand. Most iris recognition systems use algorithms developed by Daugman. The performance of iris recognition is highly depends on edge detection. Canny is the edge detectors which commonly used. The objectives of this research are to a) study the edge detection criteria and b)measure the PSNR values in estimating the noise between the original iris feature and new iris template. The eye image with [320x280] dimension is obtained from the CASIA database which has been pre-processed through the segmentation and normalization in obtaining the rubber sheet model with [20x240] in dimension. Once it has been produced, the important information is extracted from the iris. Results show that, the PSNR values of iris feature before and after the process of extraction, was 24.93 and 9.12. For sobel and prewitt, both give 18.5 after the process. Based on our findings, the impact of edge detection techniques produces higher accuracy in iris recognition system

IRIS BIOMETRICS STEGANOGRAPHIC METHOD WITH PIXEL VALUE DIFFERENCING AND HOUGH TRANSFORM FOR HIGHER SECURITY SYSTEM

In biometric security, steganography has become one of the techniques used in defending biometrics data and system. This is due to fraud to the biometric data and illegal activites occured at the biometrics point of system. The biometric data, which in this study, iris, is preprocessed using Hough Transform in producing the iris feature. The pixel values of iris feature is formed, in order to embed the iris feature with the stego key which gained from the cover image (thumbprint from the same trained sample). Studies showed that various techniques of embedding such as least significant bits and pixel value differencing are among popular researches. However, none has been designed for iris implementation in biometrics system. Therefore, a new technique is presented in this paper which integrates pixel value differencing with Hough method in the iris biometrics system. The proposed method modified the pixels values by modifying the most conservative pixels of the block. The theoritical estimation and results produce a scheme which provide a better embedding. The new simulation method provides an embedding capacity, human visual quality and PSNR value is 39.34 dB which is better than the previous methods

ant-CBIR: a new method for radial furrow extraction in iris biometric

Iris recognition has evolved from first to second generation of biometric systems which capable of recognizing unique iris features such as crypts, collarette and pigment blotches. However, there are still ongoing researches on finding the best way to search unique iris features since iris image contains high noise. The high noise iris images (noisy iris); usually give the biometric systems to deliver erroneous results, leading to categorizations where the actual user is labeled as an impostor. Therefore, this study focuses on a novel method, targeted at overcoming the aforementioned challenge. We present the use of ant colony based image retrieval (ant–CBIR) technique as a successful method in recognizing the radial furrow in noisy iris. This method simulates the behavior of artificial ants, searching for pixel values of radial furrow based on an optimum pixel range. The evaluation of accuracy performance with and without the ant-CBIR application is measured using GAR parameter on UBIRIS.v1. Results show that the GAR is 79.9% with ant-CBIR implementation. The implication of this study contributes to a new feature extraction that has the ability of human-aided computing. Moreover, ant-CBIR helps to provide cost effective, easy maintenance and exploration of a long term data collection

IRIS RECOGNITION FAILURE IN BIOMETRICS: A REVIEW

More than twenty years iris has been claimed to be the most stable modality in human lifetime. However, the iris recognition produces ‘failure to match’ problem which made the known is unknown user or the genuine is recognized as imposter in the biometric systems. Apparently, failure to recognize the real user as in the database is due to a few assumptions: aging of the sensor, changes in how a person uses the system such as the threshold settings and template aging effect. This paper focuses on template aging effect since it is on ongoing problem faced in iris recognition. Many studies attempted several techniques to overcome the problem in every phase which consists of three general phases: the pre-processing, feature extraction and feature matching. Therefore, the purpose of this paper is to study and identify the problems in iris recognition that lead to failure-to-match in biometrics

A New Model of Securing Iris Authentication Using Steganography

The integration of steganography in biometric system is a solution for enhancing security in iris. The process of biometric enrollment and verification is not highly secure due to hacking activities at the biometric point system such as overriding iris template in database. In this paper, we proposed an enhancement of temporal-spatial domain algorithm which involves the scheme of Least Significant Bits (LSB) as the new model which converts iris images to binary stream and hides into a proper lower bit plane. Here, the stego key, n, will be inserted into the binary values from the plane which concealed the information; where n is the input parameter in binary values which inserted to the iris codes, m. These values produce the output which is the new iris stego image after binary conversion. Theoretically, the proposed model is promising a high security performance implementation in the future

ant-CBIR: a new method for radial furrow extraction in iris biometric

Iris recognition has evolved from first to second generation of biometric systems which capable of recognizing unique iris features such as crypts, collarette and pigment blotches. However, there are still ongoing researches on finding the best way to search unique iris features since iris image contains high noise. The high noise iris images (noisy iris); usually give the biometric systems to deliver erroneous results, leading to categorizations where the actual user is labeled as an impostor. Therefore, this study focuses on a novel method, targeted at overcoming the aforementioned challenge. We present the use of ant colony based image retrieval (ant–CBIR) technique as a successful method in recognizing the radial furrow in noisy iris. This method simulates the behavior of artificial ants, searching for pixel values of radial furrow based on an optimum pixel range. The evaluation of accuracy performance with and without the ant-CBIR application is measured using GAR parameter on UBIRIS.v1. Results show that the GAR is 79.9% with ant-CBIR implementation. The implication of this study contributes to a new feature extraction that has the ability of human-aided computing. Moreover, ant-CBIR helps to provide cost effective, easy maintenance and exploration of a long term data collection

Load Balancing Algorithms In Software Defined Network

Compared with the traditional networks, the SDN networks have shown great advantages in many aspects, but also exist the problem of the load imbalance. If the load distribution uneven in the SDN networks, it will greatly affect the performance of network. Many SDN-based load balancing strategies have been proposed to improve the performance of the SDN networks. Therefore, in this paper a finding form comprehensive review help to improve further understanding of lead b balancing algorithms in SDN