5 research outputs found
A statistical sampling strategy for iris recognition
We present a new approach for iris recognition based on a random sampling strategy. Iris recognition is a method to identify individuals, based on the analysis of the eye iris. This technique has received a great deal of attention lately, mainly due to iris unique characterics:
highly randomized appearance and impossibility to alter its features. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. Our work uses standard integrodifferential operators to locate the iris. Then, we process iris image with histogram equalization to compensate for illumination variations.The characterization of iris features is performed by using accumulated histograms. These histograms are built from randomly selected subimages of iris. After that, a comparison is made between accumulated histograms of couples of iris samples, and a decision is taken based on their differences and on a threshold calculated experimentally. We ran experiments with a database of 210 iris, extracted from 70 individuals, and found a rate of succesful identifications in the order of 97 %.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI
An application of ARX stochastic models to iris recognition
We present a new approach for iris recognition based on stochastic autoregressive models with exogenous input (ARX). Iris recognition is a method to identify persons, based on the analysis of the eye iris.
A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. The main contribution in this work is given in the step of characterization of iris features by using ARX models. In our work every iris in database is represented by an ARX model learned from data. In the comparison and matching step, data taken from iris sample are substituted into every ARX model and residuals are generated. A decision of accept or reject is taken based on residuals and on a threshold calculated experimentally. We conduct experiments with two different databases. Under certain conditions, we found a rate of successful identifications in the order of 99.7 % for one database and 100 % for the other.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI
An application of ARX stochastic models to iris recognition
We present a new approach for iris recognition based on stochastic autoregressive models with exogenous input (ARX). Iris recognition is a method to identify persons, based on the analysis of the eye iris.
A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. The main contribution in this work is given in the step of characterization of iris features by using ARX models. In our work every iris in database is represented by an ARX model learned from data. In the comparison and matching step, data taken from iris sample are substituted into every ARX model and residuals are generated. A decision of accept or reject is taken based on residuals and on a threshold calculated experimentally. We conduct experiments with two different databases. Under certain conditions, we found a rate of successful identifications in the order of 99.7 % for one database and 100 % for the other.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI
A statistical sampling strategy for iris recognition
We present a new approach for iris recognition based on a random sampling strategy. Iris recognition is a method to identify individuals, based on the analysis of the eye iris. This technique has received a great deal of attention lately, mainly due to iris unique characterics:
highly randomized appearance and impossibility to alter its features. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. Our work uses standard integrodifferential operators to locate the iris. Then, we process iris image with histogram equalization to compensate for illumination variations.The characterization of iris features is performed by using accumulated histograms. These histograms are built from randomly selected subimages of iris. After that, a comparison is made between accumulated histograms of couples of iris samples, and a decision is taken based on their differences and on a threshold calculated experimentally. We ran experiments with a database of 210 iris, extracted from 70 individuals, and found a rate of succesful identifications in the order of 97 %.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI
Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms
This paper proposes a new sensor placement approach for leak location in water
distribution networks (WDNs). The sensor placement problem is formulated as an integer
optimization problem. The optimization criterion consists in minimizing the number of
non-isolable leaks according to the isolability criteria introduced. Because of the large
size and non-linear integer nature of the resulting optimization problem, genetic algorithms
(GAs) are used as the solution approach. The obtained results are compared with a
semi-exhaustive search method with higher computational effort, proving that GA allows
one to find near-optimal solutions with less computational load. Moreover, three ways of
increasing the robustness of the GA-based sensor placement method have been proposed
using a time horizon analysis, a distance-based scoring and considering different leaks sizes.
A great advantage of the proposed methodology is that it does not depend on the isolation
method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in
two networks allow us to evaluate the performance of the proposed approach