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Biometrics usually provides several advantages over traditional forms of identity authentication. However, there are some concerns about the security of personal biometric data, while these systems need to ensure their integrity and public acceptance. The idea of cancellable biometrics has been introduced to overcome these security concerns. In addition, an important way to increase security and performance in biometric systems is the multi-biometric approach, which combines different sources of biometric information. This paper investigates the benefits that optimized ensemble systems and cancellable transformation functions can provide to multibiometric authentication systems. The main aim of the work reported here is to provide more security and better performance in the multi-biometric authentication process. We use as examples two different biometric modalities (face and voice) separately and in the multi-modal context (multi-biometric). The datasets used in this analysis were TIMIT for voice and the AR Face dataset for Face. As a result of this analysis, we will observe that the use of cancellable transformations in the multi-biometric dataset increases the accuracy level for the ensemble systems. Abstract

Genetic Algorithm and Ensemble Systems for Multi-biometric Cancellable Recognition