Artificial Neural Network Based Iris Recognition System

This paper deals with biometric personal identification based on iris recognition using artificial neural network. The Iris recognition system includes pupil detection, and the enhancement, region of interest of iris detected from an eye image then, the iris recognition using neural network approach. For the localization of the inner and outer boundaries of the iris region is being proposed a fast algorithm by us. Located pupil is detected from an eye image, and, after enhancement, located iris part is detected from an eye image, it is represented by a dataset. In this paper, we proposed a neural network based iris recognition approach by analyzing iris patterns. Hough transforms are used for localizing the part of iris region; then, histogram equalization was applied to the iris an image for making the shapes an image more distinctive. The gray-level iris images, experimented in this work, were obtained from the Institute Automation Chinese Academy of Science (CASIA) iris images database version 1.0

An enhanced iris recognition and authentication system using energy measure

In order to fight identity fraud, the use of a reliable personal identifier has become a necessity. Using Personal Identification Number (PIN) or a password is no longer secure enough to identify an individual. Iris recognition is considered to be one of the best and accurate form of biometric measurements compared to others, it has become an interesting research area. Iris recognition and authentication has a major issue in its code generation and verification accuracy, in order to enhance the authentication process, a binary bit sequence of iris is generated, which contain several vital information that is used to calculate the Mean Energy and Maximum Energy that goes into the eye with an adopted Threshold Value. The information generated can further be used to find out different eye ailments. An iris is obtained using a predefined iris image which is scanned through eight (8) different stages and wavelet packet decomposition is used to generate 64 wavelet packages bit iris code so as to match the iris codes with Hamming distance criteria and evaluate different energy values. The system showed 98% True Acceptance Rate and 1% False Rejection Rate and this is because some of the irises weren’t properly captured during iris acquisition phase. The system is implemented using UBIRIS v.1 Database.Keywords: Local Image Properties, Authentication Enhancement, Iris Authentication, Local Image, Iris Recognition, Binary Bit Sequenc

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p