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.

Conflict Detection and Bayesian Conditioning for Estimating the Reliability of Each LVQ Network in a Group Engaged at Iris Biometric Identification / VALLESI G., MONTESANTO A; Dragoni, Aldo Franco. - STAMPA. - (2008), pp. 619-624. (Intervento presentato al convegno 8th International Conference on Hybrid Intelligent Systems tenutosi a Barcellona nel 10-12/9/2008) [10.1109/HIS.2008.35].

Conflict Detection and Bayesian Conditioning for Estimating the Reliability of Each LVQ Network in a Group Engaged at Iris Biometric Identification

DRAGONI, Aldo Franco
2008-01-01

Abstract

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.
2008
9780769533261
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/47742
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