Predictive models for multibiometric systems

• The work proposes Multinomial and Geometric models for multibiometric systems.
• The models predict bounds on indices in which a test subject is likely to be present.
• Bounds are used in the recognition system to predict a smaller subset of candidates.
• The models are verified with various publically available multibiometric data.

Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations.