Towards Predicting Optimal Fusion Candidates: A Case Study on Biometric Authentication Tasks

Combining multiple information sources, typically from several data streams is a very promising approach, both in experiments and to some extend in various real-life applications. However, combining too many systems (baseexperts) will also increase both hardware and computation costs. One way to selecting a subset of optimal base-experts out of N is to carry out the experiments explicitly. There are 2N − 1 possible combinations. In this paper, we propose an analytical solution to this task when weighted sum fusion mechanism is used. The proposed approach is at least valid in the domain of person authentication. It has a complexity that is additive between the number of examples and the number of possible combinations while the conventional approach, using brute-force experimenting, is multiplicative between these two terms. Hence, our approach will scale better with large fusion problems. Experiments on the BANCA multi-modal database verified our approach. While we will consider her...

• Type: Conference
• Year: 2004
• Where: MLMI
• Authors: Norman Poh, Samy Bengio
• Keywords: Fusion Problem, MLMI 2004, Possible Combinations, Sum Fusion Mechanism