Edge detection techniques for iris recognition system

Nowaday s security and authentication are the major part s of our daily life. Iris is one of the most reliable organ or part of human body which can be used for identification and authentication purpose. To develop an iris authentication algorithm for personal identification, this paper examines two edge detection techniques for iris recognition system. Between the Sobel and the Canny edge detection techniques, the experimental result shows that the Canny’s technique has better ability to detect poi nts in a digital image where image gray level changes even at slow rate

A Survey on IRIS Recognition System: Comparative Study

Because of an increasing emphasis on security, Iris recognition has gained a great attention in both research and practical applications over the past decade. The demand for iris recognition in the various fields of access control reducing fraudulent transactions in electronic commences, security at border areas etc is increasing day by day due to its high accuracy, reliability and uniqueness. A review of various segmentation approaches used in iris recognition is done in this paper. The performance of the iris recognition systems depends heavily on segmentation and normalization techniques

An LBP based Iris Recognition System using Feed Forward Back Propagation Neural Network

An iris recognition system using LBP feature extraction technique with Feed Forward Back Propagation Neural Network is presented. For feature extraction from the eye images the iris localization and segmentation is very important task so in proposed work Hough circular transform (HCT) is used to segment the iris region from the eye mages. In this proposed work Local Binary Pattern (LBP) feature extraction technique is used to extract feature from the segmented iris region, then feed forward back propagation neural network is use as a classifier and in any classifier there to phases training and testing. The LBP feature extraction technique is a straightforward technique and every proficient feature operator which labels the pixels of an iris image by thresholding the neighbourhood of each pixel and considers the feature as a result in form of binary number. Due to its discriminative efficiency and computational simplicity the LBP feature extractor has become a popular approach in various recognition systems. This proposed method decreased the FAR as well as FRR, & has increases the system performance on the given dataset. The average accuracy of proposed iris recognition system is more than 97%

Pigment Melanin: Pattern for Iris Recognition

Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive feature-code for each subject. An important question is how much the melanin patterns, extracted from VL, are independent of iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost the recognition performance. We have collected our own database (UTIRIS) consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of cromophores and improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on Instruments and Measurements, Volume 59, Issue number 4, April 201

Biometric Authentication using Nonparametric Methods

The physiological and behavioral trait is employed to develop biometric authentication systems. The proposed work deals with the authentication of iris and signature based on minimum variance criteria. The iris patterns are preprocessed based on area of the connected components. The segmented image used for authentication consists of the region with large variations in the gray level values. The image region is split into quadtree components. The components with minimum variance are determined from the training samples. Hu moments are applied on the components. The summation of moment values corresponding to minimum variance components are provided as input vector to k-means and fuzzy kmeans classifiers. The best performance was obtained for MMU database consisting of 45 subjects. The number of subjects with zero False Rejection Rate [FRR] was 44 and number of subjects with zero False Acceptance Rate [FAR] was 45. This paper addresses the computational load reduction in off-line signature verification based on minimal features using k-means, fuzzy k-means, k-nn, fuzzy k-nn and novel average-max approaches. FRR of 8.13% and FAR of 10% was achieved using k-nn classifier. The signature is a biometric, where variations in a genuine case, is a natural expectation. In the genuine signature, certain parts of signature vary from one instance to another. The system aims to provide simple, fast and robust system using less number of features when compared to state of art works.Comment: 20 page

Palmprint Authentication System Based on Local and Global Feature Fusion Using DOST

Palmprint is the region between wrist and fingers. In this paper, a palmprint personal identification system is proposed based on the local and global information fusion. The local and global information is critical for the image observation based on the results of the relationship between physical stimuli and perceptions. The local features of the enhanced palmprint are extracted using discrete orthonormal Stockwell transform. The global feature is obtained by reducing the scale of discrete orthonormal Stockwell transform to infinity. The local and global matching distances of the two palmprint images are fused to get the final matching distance of the proposed scheme. The results show that the fusion of local and global features outperforms the existing works on the available three datasets

Machine Learning for Biometrics

Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palmprint and voice, but there are many other possible biometrics, including gait, ear image, retina, DNA, and even behaviours. This chapter presents a survey of machine learning methods used for biometrics applications, and identifies relevant research issues. We focus on three areas of interest: offline methods for biometric template construction and recognition, information fusion methods for integrating multiple biometrics to obtain robust results, and methods for dealing with temporal information. By introducing exemplary and influential machine learning approaches in the context of specific biometrics applications, we hope to provide the reader with the means to create novel machine learning solutions to challenging biometrics problems

Verificación de identidad de personas mediante sistemas biométricos para el control de acceso a una universidad

El presente documento es el resultado de la investigación realizada en la Pontificia Universidad Católica del Perú para la implementación de sistemas biométricos (lectores de huellas dactilares) como elementos de seguridad. Dada la problemática existente en la universidad (robos, plagios, amontonamiento de personas para ingresar, etc.), al implementar sistemas biométricos se estaría mejorando sustancialmente esta situación, pues aparte de tener un lugar más seguro y confiable, se estaría involucrando a la comunidad universitaria en el uso de tecnología de vanguardia. En el contenido del presente documento de investigación se abordará con mayor detalle los temas relacionados a los sistemas de seguridad empleados actualmente tanto en lugares públicos como privados, y la descripción y evaluación (costos y beneficios) de los sistemas biométricos más usados en el mundo. Habiendo hecho el análisis de costos y beneficios, se llega a la conclusión de que la implementación de sistemas biométricos basados en las huellas dactilares sería la opción óptima, tanto para mejorar la seguridad como para agilizar el ingreso al campus universitario.Tesi