Germano Vallesi

Fingerprints Recognition Using Minutiae Extraction: a Fuzzy Approach.

The aim of this paper is to study the fingerprint verification based on local ridge discontinuities features (minutiae) only using grey scale images. We extract minutiae using two algorithms those following ridge lines and then recording ridge endings and bifurcations. Moreover we use a third algorithm able to develop a minutiae verification processing a local area using a neural network ( multilayer perceptron). Fingerprint distortion is filtered using a minutiae whole representation based on regular invariant moments. The results of the three minutiae extraction algorithms are joined during the minutiae pattern matching phase for fingerprint verification. Here we propose a new method of matching that use fuzzy operator to bypass the problem of different numbers of minutiae extracted from the algorithms. Experimental evidences show fingerprint recognition up to 95%.

Multiple Neural Networks System for Dynamic Environments

We propose a “Multiple Neural Networks” system for dynamic environments, where one or more neural nets may no longer be able to properly operate, due to sensible partial changes in the characteristics of the individuals. We assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net’s “degree of reliability” is defined as “the probability that the net is giving the desired output”, in case of conflicts between the outputs of the various nets the re-evaluation of their “degrees of reliability” can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used for making the final choice, by applying the “Inclusion based” algorithm over all the maximally consistent subsets of the global outcome. Finally, the nets recognized as responsible for the conflicts will be automatically forced to learn about the changes in the individuals’ characteristics and avoid to make the same error in the immediate future.

Hybrid system for a never-ending unsupervised learning

We propose a Hybrid System for dynamic environments, where a “Multiple Neural Networks” system works with Bayes Rule. One or more neural nets may no longer be able to properly operate, due to partial changes in some of the characteristics of the individuals. We assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the nets degree of reliability is defined as the probability that the net is giving the desired output, in case of conflicts between the outputs of the various nets the re-evaluation of their degrees of reliability can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used for making the final choice, by applying two algorithms, the “Inclusion based” and the “Weighted” one over all the maximally consistent subsets of the global outcome.

Multiple Neural Networks and Bayesian Belief Revision for a never-ending unsupervised learning

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.

An hybrid system for continuous learning

We propose a Multiple Neural Networks system for dynamic environments, where one or more neural nets could no longer be able to properly operate, due to partial changes in some of the characteristics of the individuals We assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group Since the nets degree of reliability is defined as the probability that the net is giving the desired output, in case of conflicts between the outputs of the various nets the re-evaluation of their degrees of reliability can be simply performed on the basis of the Bayes Rule The new vector of reliability will be used for making the final choice, by applying two algorithms, the Inclusion based and the Weighted one over all the maximally consistent subsets of the global outcome.