Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition

This paper assumes the hypothesis that human learning is perception based, and consequently, the learning process and perceptions should not be represented and investigated independently or modeled in different simulation spaces. In order to keep the analogy between the artificial and human learning, the former is assumed here as being based on the artificial perception. Hence, instead of choosing to apply or develop a Computational Theory of (human) Perceptions, we choose to mirror the human perceptions in a numeric (computational) space as artificial perceptions and to analyze the interdependence between artificial learning and artificial perception in the same numeric space, using one of the simplest tools of Artificial Intelligence and Soft Computing, namely the perceptrons. As practical applications, we choose to work around two examples: Optical Character Recognition and Iris Recognition. In both cases a simple Turing test shows that artificial perceptions of the difference between two characters and between two irides are fuzzy, whereas the corresponding human perceptions are, in fact, crisp.Comment: 5th Int. Conf. on Soft Computing and Applications (Szeged, HU), 22-24 Aug 201

Neural Class-Specific Regression for face verification

Face verification is a problem approached in the literature mainly using nonlinear class-specific subspace learning techniques. While it has been shown that kernel-based Class-Specific Discriminant Analysis is able to provide excellent performance in small- and medium-scale face verification problems, its application in today's large-scale problems is difficult due to its training space and computational requirements. In this paper, generalizing our previous work on kernel-based class-specific discriminant analysis, we show that class-specific subspace learning can be cast as a regression problem. This allows us to derive linear, (reduced) kernel and neural network-based class-specific discriminant analysis methods using efficient batch and/or iterative training schemes, suited for large-scale learning problems. We test the performance of these methods in two datasets describing medium- and large-scale face verification problems.Comment: 9 pages, 4 figure

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

A comparative analysis of neural and statistical classifiers for dimensionality reduction-based face recognition systems.

Human face recognition has received a wide range of attention since 1990s. Recent approaches focus on a combination of dimensionality reduction-based feature extraction algorithms and various types of classifiers. This thesis provides an in depth comparative analysis of neural and statistical classifiers by combining them with existing dimensionality reduction-based algorithms. A set of unified face recognition systems were established for evaluating alternate combinations in terms of recognition performance, processing time, and conditions to achieve certain performance levels. A preprocessing system and four dimensionality reduction-based methods based on Principal Component Analysis (PCA), Two-dimensional PCA, Fisher\u27s Linear Discriminant and Laplacianfaces were utilized and implemented. Classification was achieved by using various types of classifiers including Euclidean Distance, MLP neural network, K-nearest-neighborhood classifier and Fuzzy K-Nearest Neighbor classifier. The statistical model is relatively simple and requires less computation complexity and storage. Experimental results were shown after the algorithms were tested on two databases of known individuals, Yale and AR database. After comparing these algorithms in every aspect, the results of the simulations showed that considering recognition rates, generalization ability, classification performance, the power of noise immunity and processing time, the best results were obtained with the Laplacianfaces, using either Fuzzy K-NN.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .X86. Source: Masters Abstracts International, Volume: 45-01, page: 0428. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006