Enhancing Identity Prediction Using a Novel Approach to Combining Hard- 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.