Minimizing the impact of low interoperability between optical fingerprints sensors

In fingerprint recognition, interoperability is the ability of a system to work with a diverse set of fingerprint devices. Variations induced by fingerprint sensors include image resolution, scanning area, gray levels, etc. Such variations can impact (i) the quality of the extracted features, and (ii) cross-device matching performance. This is true even when dealing with fingerprint sensors of the same sensing technology (e.g. optical). Previous research did not provide a model to accommodate sensor distortions to increase cross-device matching performance. In this paper, we propose a method that increases interoperability in systems which deploy optical fingerprint sensors. We design and evaluate a set of characteristics suitable for measuring differences in fingerprint image acquisition. Further, we propose a classification scheme, which combines the defined features with match scores. The classification performance is evaluated on a set of fingerprints acquired using four different optical devices and scanned rolled ink prints, from approximately 500 subjects. Experimental results confirm the significant impact of low interoperability on match rates and show that the proposed approach is able to reduce cross-device match error rates by a significant margin.