Impact of combining quality measures on biometric sample matching

Biometric matching involves a comparison of two biometric data samples. In practical applications, one or both of the samples may be of degraded quality, in respect to the nominal quality of similar samples acquired in controlled conditions. It has been shown in prior art that in such situations, the integration of quality information into the process of bio-metric matching can lead to significantly improved classification performance of the biometric matcher. To facilitate such an integration, quality measures originating from both compared biometric samples are usually combined into one quality score. In this paper, we analyze the merit of doing so. We revisit the problem from a pattern classification perspective, and show that using individual quality measures as separate classification features frequently leads to a superior performance of a biometric system in comparison with the system in which quality measures are mapped into one quality score. We provide experimental support of this claim using synthetic data, as well as real biometric database, on the examples of face, fingerprint and multi-modal matching.