Combining classifiers with different footstep feature sets and multiple samples for person identification

Combination of classifiers is usually a good strategy to improve accuracy in pattern recognition systems. In this paper, we present a new approach to footstep-based biometric identification by combining pattern classifiers with different feature sets. Footstep profiles are obtained from a pressure-sensitive floor. Our identification system consists of two different combination stages. At the first stage, three pattern classifiers, trained with feature sets presenting different characteristics of input signal, are combined. The feature sets include the spatial domain properties of the footstep profile as well as the frequency domain presentation of the signal and its derivative. At the second stage, multiple input samples are combined, using the posterior probability outputs from the first stage, to make the final decision. The building blocks of the classification system are examined, and the methodological justifications are analyzed. The experimental results show improvements in identification accuracies compared to previously reported work.