학술논문

Multiple Neural Networks and Bayesian Belief Revision for a never-ending unsupervised learning
Document Type
Conference
Source
2010 10th International Conference on Intelligent Systems Design and Applications Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on. :421-426 Nov, 2010
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Engineered Materials, Dielectrics and Plasmas
Components, Circuits, Devices and Systems
Reliability
Face recognition
Face
Artificial neural networks
Mouth
Bayesian methods
Intelligent systems
Language
ISSN
2164-7143
2164-7151
Abstract
A system of Multiple Neural Networks has been proposed to solve the face recognition problem. Our idea is that a set of expert networks specialized to recognize specific parts of face are better than a single network. This is because a single network could no longer be able to correctly recognize the subject when some characteristics partially change. For this purpose we assume that each network has a reliability factor defined as the probability that the network is giving the desired output. In case of conflicts between the outputs of the networks the reliability factor can be dynamically re-evaluated on the base of the Bayes Rrule. The new reliabilities will be used to establish who is the subject. Moreover the network disagreed with the group and specialized to recognize the changed characteristic of the subject will be retrained and then forced to correctly recognize the subject. Then the system is subjected to continuous learning.