Predicting identification errors in a multibiometric system based on ranks and scores

The goal of a biometric identification system is to determine the identity of the input biometric data. In such a system, the input probe (e.g., a face image) is compared against the labeled gallery data (e.g., face images in a watch-list) resulting in a set of ranked scores pertaining to the different identities in the gallery database. The identity corresponding to the best score is then associated with that of the probe. The aim of this work is to predict identification errors and improve the recognition accuracy of the biométrie system. The method utilizes the rank and score information generated by the identification operation in order to validate the output. Further, we demonstrate the proposed predictor can be effectively applied in multimodal scenarios. Experiments performed on two multimodal databases show the effectiveness of our framework in improving identification performance of biométrie systems.