Bayesian Conditioning for estimating the relative degrees of Reliability in a group of Neural Networks engaged at Iris Biometric Identification

The main problem with iris biometric identification systems is the presence of noises in the image of the eye (eyelid, eyelashes, etc…). To remove it many authors apply appropriate preprocessing to the image, but unfortunately this yields losses of information. Our work aims at correctly recognizing the subject also in presence of high rates of noise. The basic idea is that of partitioning the image of iris into 8 not-interleaved segments of the same size. Each segment is given to an LVQ network which generates prototypes with a high resistance to noise. Notwithstanding this, the 8 LVQ nets may still disagree in identifying the subject. In this paper we apply a method developed by the “belief revision” community to identify conflicts and rearrange the degrees of reliability of each expert (the LVQ nets) through a Bayesian algorithm. This estimated ranking of reliability is useful to take the final decision. Our work has produced an interesting 84 % of positive identificat..