FACE RECOGNITION THROUGH LEARNED BOUNDARY CHARACTERISTICS

This paper presents a new approach to face recognition, combining the techniques of computer vision and machine learning. A steady improvement in recognition performance is demonstrated. It is achieved by learning individual faces in terms of the local shapes of image boundaries. High-level facial features, such as nose, are not explicitly used in this scheme. Several machine learning methods are tested and compared. The overall objectives are formulated as follows: Classify the different tasks of "face recognition" and suggest an orderly terminology to distinguish between them. Design a set of easily and reliably obtainable descriptors and their automatic extraction from the images. Compare plausible machine learning methods; tailor them to this domain. Design experiments that would best reflect the needs of real world applications, and suggest a general methodology for further research. Perform the experiments and compare the performance

Face recognition through learnt boundary characteristics

SIGLEAvailable from British Library Document Supply Centre- DSC:9109.3968(EU-DCS-CSM--188) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Improved emergency management by a loosely coupled logistic system

We investigate a robust and intelligent logistic system for emergency management where existing commercial logistic systems are loosely coupled with logistic systems of emergency management organizations and armed forces. This system is used to supply the population in case of a disaster where a high impact of environmental conditions on logistics can be seen. Very important are robustness as the ability of a logistic system to remain effective under these conditions and intelligent behavior for automated ad-hoc decisions facing unforeseen events. Scenario technique, roadmapping, as well as surveys are used as qualitative methodologies to identify current weaknesses in emergency management logistics and to forecast future development of loosely coupled logistic systems. Text mining and web mining analysis as quantitative methodologies are used to improve forecasting. As a result, options are proposed for governmental organizations and companies to enable such a loosely coupled logistic system within the next 20 years

Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach

Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set