The strong, neutral, or weak (SNoW) face impostor pairs problem is intended to explore the causes and impact of impostor face pairs that are inherently strong (easily recognized as nonmatches) or weak (possible false matches). The SNoW technique develops three partitions within the impostor score distribution of a given data set. Results provide evidence that varying degrees of impostor scores impact the overall performance of a face recognition system. This paper extends our earlier work to incorporate improvements regarding outlier detection for partitioning, explores the SNoW concept for the additional modalities of fingerprint and iris, and presents methods for how to begin to reveal the causes of weak impostor pairs. We also show a clear operational difference between strong and weak comparisons as well as identify partition stability across multiple algorithms.

The issue of interoperability between iris sensors is an important topic in large-scale and long-term applications of iris biometric systems. This work compares three commercially available iris sensors and three iris matching systems and investigates the impact of cross-sensor matching on system performance in comparison to single-sensor performance. Several factors which may impact single-sensor and cross-sensor performance are analyzed, including changes in the acquisition environment and differences in dilation ratio between iris images. The sensors are evaluated using three different iris matching algorithms, and conclusions are drawn regarding the interaction between the sensors and the matching algorithm in both the cross-sensor and single-sensor scenarios. Finally, the relative performances of the three sensors are compared.