  Recently, face recognition systems have demonstrated remarkable performances
and thus gained a vital role in our daily life. They already surpass human face
verification accountability in many scenarios. However, they lack explanations
for their predictions. Compared to human operators, typical face recognition
network system generate only binary decisions without further explanation and
insights into those decisions. This work focuses on explanations for face
recognition systems, vital for developers and operators. First, we introduce a
confidence score for those systems based on facial feature distances between
two input images and the distribution of distances across a dataset. Secondly,
we establish a novel visualization approach to obtain more meaningful
predictions from a face recognition system, which maps the distance deviation
based on a systematic occlusion of images. The result is blended with the
original images and highlights similar and dissimilar facial regions. Lastly,
we calculate confidence scores and explanation maps for several
state-of-the-art face verification datasets and release the results on a web
platform. We optimize the platform for a user-friendly interaction and hope to
further improve the understanding of machine learning decisions. The source
code is available on GitHub, and the web platform is publicly available at
http://explainable-face-verification.ey.r.appspot.com.
