Enhancing Identity Prediction Using a Novel Approach to Combining Biometric and Soft-Biometric Information

The effectiveness with which individual identity can

be predicted in, for example, an antiterrorist scenario can benefit

from seeking a broad base of identity evidence. The issue of improving

performance can be addressed in a number of ways, but system

configurations based on integrating different information sources

(often involving more than one biometric modality) are a widely

adopted means of achieving this. This paper presents a new approach

to improving identification performance, where both direct

biometric samples and “soft-biometric” knowledge are combined.

Specifically, however, we propose a strategy based on an intelligent

agent-based decision-making process, which predicts both absolute

identity and also other individual characteristics from biometric

samples, as a basis for a more refined and enhanced overall identification

decision based on flexible negotiation among class-related

agents.