Biometric systems simulations performing close to real world conditions

In this paper we propose a set of biometric recognition experiments in similar conditions to real operating systems. This implies a jump from the usual laboratory conditions to a more real situation where the amount of variability between training and testing samples is large. We present experiments with face and hand-geometry recognition training a ldquouniversal classifierrdquo able to decide if two input samples belong to the same person or not. During test we recognize samples of a different database not used during classifier training. Training with the ORL face database and testing with the AR database provides a 5.1% error rate in verification operation, while training and testing with the same database yields 2.5%. For hand-geometry databases we obtain 1.33% and 0.78% for different and same testing and training databases respectively.