Face recognition: Impostor-based measures of uniqueness and quality

We present a framework, called uniqueness-based nonmatch estimates (UNE), which demonstrates the ability to improve face recognition performance of any face matcher. The first aspect of the framework is a novel metric for measuring the uniqueness of a given individual, called the impostor-based uniqueness measure (IUM). The UNE the maps face match scores from any any face matcher into non-match probability estimates that are conditionally dependent on the probe image's IUM. Using this framework we demonstrate: (i) an improved generalization of matching thresholds (and, subsequently, improved matching accuracy), (ii) a score normalization technique that improves the interoperability for users of different face matchers, and (iii) the predictive ability of IUM towards face recognition accuracy. Studies are conducted on an operational dataset with 16,000 subjects using three different face matchers (two commercial, one proprietary) to demonstrate the effectiveness of the proposed framework.