The use of quality measures in biometrics is rapidly becoming the standard method of ensuring reliable performance of the biometric systems, especially in the presence of variable environmental conditions of signal capture. It is often necessary to integrate multiple quality measures into the classification process in order to capture the relevant aspects of signal quality. The inclusion of multiple quality features quickly increases the risks of the dimensionality curse. No mature strategy of

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... oping with multiple quality measures has been proposed. In this paper we propose to use a scheme, where the dimensionality of the vector of quality measures is reduced using the Locality Preserving Projections. We show that the proposed technique offers higher accuracy and better generalization properties than existing techniques of classification with quality measures, in same-and cross-device biometric matching scenarios.