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 Rule. 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.
[1]
M. W. Shields,et al.
A theoretical framework for multiple neural network systems
,
2008,
Neurocomputing.
[2]
Aldo Franco Dragoni,et al.
Belief revision: from theory to practice
,
1997,
The Knowledge Engineering Review.
[3]
A. A. El-Harby,et al.
Face Recognition: A Literature Review
,
2008
.
[4]
David Zhang,et al.
Modular Neural Networks and Their Applications in Biometrics
,
2007,
Trends in Neural Computation.
[5]
Harry Wechsler,et al.
The FERET database and evaluation procedure for face-recognition algorithms
,
1998,
Image Vis. Comput..