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In the context of ensemble systems, feature selection methods can be used to provide different subsets of attributes for the individual classifiers, aiming to reduce redundancy among the attributes of a pattern and to increase the diversity in such systems. Among the several techniques that have been proposed in the literature, optimization methods have been used to find the optimal subset of attributes for an ensemble system. In this paper, an investigation of two optimization techniques, genetic algorithm and ant colony optimization, will be used to guide the distribution of the features among the classifiers. This analysis will be conducted in the context of heterogeneous ensembles and using different ensemble sizes.

Biometric-based identification systems can offer several advantages over traditional forms of identity authentication. However, concerns have been raised about the privacy of the personal biometric data, since these systems need to ensure their integrity and public acceptance. In order to address these issues, the notion of cancellable biometrics was introduced. It describes biometric templates that can be cancelled and replaced, in case of being lost or stolen. However, this concept still raises new issues, since they make the authentication problem more complex and difficult to solve. Thus, more effective authentication structures are needed to perform these tasks. In this paper, we investigate the use of ensemble systems in cancellable biometrics, using online signature identification. In order to improve the effectiveness of the ensemble systems, we used genetic algorithms in the choice of an optimized set of weights that are used along with the output of the individual classifiers to define the final output of the system. In addition, we proposed the use of genetic algorithm in the procedure to create the cancellable biometric data, aiming to obtain more efficient cancellable data. The main of this paper is to provide more security in the biometricbased identification process.I.

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