Training Neural Networks for minimum average risk with a special application to context dependent learning

The least squares criterion, as used by the backpropagation learning rule in multi-layer feed forward neural networks, does not always yield a solution that is in accordance with the desired behaviour of the neural network. This is for example the case when differentiation between different types of errors is required and the costs of the error types must be taken into account. In this paper the application of other error measures, specifically matched to the application, is investigated. The error measures used are based on the average risk, a function that is a weighted combination of the probabilities on the different types of errors that may occur. Special attention is payed to applications where the input patterns are not independent, and the average risk does not depend on the output of a single input pattern, but on its neighbourhood, or context. The ideas are illustrated with pulse detection in a one dimensional signal

Neural networks applied to the classification of remotely sensed data

A neural network with topology 2-8-8 is evaluated against the standard of supervised non-parametric maximum likelihood classification. The purpose of the evaluation is to compare the performance in terms of training speed and quality of classification. Classification is done on multispectral data from the Thematic Mapper(TM3,TM4) in combination with a ground reference class map. This type of data is familiar to professionals in the field of remote sensing. This means that the position of clusters in feature space is well known and understood, and that the spatial pattern is equally well known. As a spin-off, the application of a neural net to a classical task of statistical pattern recognition helps to demystify neurai networks

Virtual illumination grid for correction of uncontrolled illumination in facial images

Face recognition under uncontrolled illumination conditions is still considered an unsolved problem. In order to correct for these illumination conditions, we propose a virtual illumination grid (VIG) approach to model the unknown illumination conditions. Furthermore, we use coupled subspace models of both the facial surface and albedo to estimate the face shape. In order to obtain a representation of the face under frontal illumination, we relight the estimated face shape. We show that the frontal illuminated facial images achieve better performance in face recognition. We have performed the challenging Experiment 4 of the FRGCv2 database, which compares uncontrolled probe images to controlled gallery images. Our illumination correction method results in considerably better recognition rates for a number of well-known face recognition methods. By fusing our global illumination correction method with a local illumination correction method, further improvements are achieved

Computation of likelihood ratio from small sample set of within-source variability

In this paper we describe a new method of likelihood ratio computation for score-based biometric recognition systems given a small number of samples in within-source variability dataset. Generally the number of samples in within-source variability dataset is less than the number of samples in between-source variability dataset and therefore the probability density function (pdf) of within-source variability dataset cannot be estimated reliably compared to the pdf of between-source variability. The proposed method estimates the pdf of within-source variability from estimates of the within-source variability mean and variance and the pdf of between-source variability by minimizing the Kullback-Leibler distance [1] of the pdf of the within-source variability to that of the between-source variability given within-source variability mean and variance. It thus finds a conservative estimate of the pdf of within-source variability. Working out this optimization problem results in an log likelihood ration that is a second order polynomial of a given score value. We apply this approach of likelihood ratio computation in the area of face recognition. An existing commercial face recognition system [2] is used to obtain scores for the sets of within-source variability and between-source variability from a set of image data taken from SCFace database [3]. It contains images taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. For each subject, there are also mug shots taken in same conditions as would be expected for any law enforcement or national security use. We explore the feasibility of using an existing biometric face recognition system in forensic application by discussing some specific cases in forensic framework

Dynamically generated resonances from the vector octet-baryon octet interaction

We study the interaction of vector mesons with the octet of stable baryons in the framework of the local hidden gauge formalism using a coupled channels unitary approach. We examine the scattering amplitudes and their poles, which can be associated to known J^P=1/2^-,3/2^- baryon resonances, in some cases, or give predictions in other ones. The formalism employed produces doublets of degenerate J^P=1/2^-,3/2^- states, a pattern which is observed experimentally in several cases. The findings of this work should also be useful to guide present experimental programs searching for new resonances, in particular in the strange sector where the current information is very poor.Comment: 21 pages, 4 figure

Database Cross Matching: A Novel Source of Fictitious Forensic Cases

Due to privacy concern and data protection laws, it is very difficult to obtain real forensic data for forensic face recognition research. In this paper, we introduce the concept of Database Cross Matching (DCM) as a novel source of fictitious but challenging forensic cases. DCM refers to the task of finding the subjects that are common in two different data sets. For most pairs of independent data sets, there will be no common subjects. However, for some data sets captured at the same institution, but independently and at different times, there is a high probability of finding some common subjects. We demonstrate the feasibility of DCM using the PIE and MultiPIE data set that were captured at the same institution in 2000 and 2004 respectively. We denote the task of finding the subjects that are common in PIE and MultiPIE data as PIE $\cap$ MultiPIE problem. Evaluation of the five face recognition systems applied to the PIE $\cap$ MultiPIE problem show that DCM can indeed create very challenging forensic problems