The Fusion of Face Recognition Algorithms (FOFRA) 2018 Prize Challenge aims to improve biometric fusion technology. The challenge addresses fusion of data from face recognition algorithms each applied to common input imagery. This goal is thus multi-algorithm fusion. Fusion is conducted either at the template level, and separately, at the score level.
Motivation: Face recognition error rates, particularly on uncontrolled face imagery, are well above zero. While algorithm development has seen considerable investment, other mechanisms for improving accuracy are known. Among them, there is a large academic literature on biometric fusion, covering multimodal and multi-algorithmic fusion. It shows that substantial accuracy gains can be made over using a single mode, or a single algorithm alone, and this can be achieved, in large part, using quite simple methods. The gains reduce when the fused inputs are correlated. The vast majority of the literature addresses biometric verification, rather than identification. Moreover, the literature covers score-level fusion rather than feature (i.e. template) level fusion. The latter, on information theoretic grounds, offers greater accuracy gains at the expense of some complexity.