Authors:
M. Alekseichevs
and
A. Glazs
Affiliation:
Riga Tehnical University, Latvia
Keyword(s):
Face recognition, Neural networks, Neural network ensemble, Plurality voting, Ensemble averaging, Weighted voting, Committee decision.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Authors describe a novel approach for human faces recognition using ensembles (or committee) of artificial neural networks. In the task of human faces recognition there are several problems that should be considered: 1) overlapping of different sets (classes), for example, when distinguishing faces of twins; 2) the training time of neural networks can be limited. In this case it is not possible to reach correct recognition of training set during neural networks training. Therefore, the two-level hierarchical structure is used to recognize objects of examination (testing) set. As a result of neural networks training at the lower level a decisions set is formed. On the basis of the decisions set the final committee solution is constructed at the upper level. A special algorithm of weighted voting is proposed to form the committee decision. The experimental results show that the proposed algorithm is more effective in comparison with other known committee methods, when number of trainin
g iterations is limited.
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