  Explainable Face Recognition is gaining growing attention as the use of the
technology is gaining ground in security-critical applications. Understanding
why two faces images are matched or not matched by a given face recognition
system is important to operators, users, anddevelopers to increase trust,
accountability, develop better systems, and highlight unfair behavior. In this
work, we propose xSSAB, an approach to back-propagate similarity score-based
arguments that support or oppose the face matching decision to visualize
spatial maps that indicate similar and dissimilar areas as interpreted by the
underlying FR model. Furthermore, we present Patch-LFW, a new explainable face
verification benchmark that enables along with a novel evaluation protocol, the
first quantitative evaluation of the validity of similarity and dissimilarity
maps in explainable face recognition approaches. We compare our efficient
approach to state-of-the-art approaches demonstrating a superior trade-off
between efficiency and performance. The code as well as the proposed Patch-LFW
is publicly available at: https://github.com/marcohuber/xSSAB.
