A Proposal for the Management of the Measurement Uncertainty in Classification and Recognition Problems

This paper proposes a new approach to classification and recognition problems. It considers the measurement uncertainty affecting input data to improve the overall effectiveness of this type of process. The proposed method is based on an effective probabilistic approach for the evaluation of the confidence level of system outputs and the suitable use of related information to improve the performance in terms of the correct decision rate. As a case study, it is applied to a particular face recognition classification algorithm based on linear discrimination analysis. The performance comparison with a traditional approach has proven the value of the proposal.