  An essential factor to achieve high performance in face recognition systems
is the quality of its samples. Since these systems are involved in daily life
there is a strong need of making face recognition processes understandable for
humans. In this work, we introduce the concept of pixel-level face image
quality that determines the utility of pixels in a face image for recognition.
We propose a training-free approach to assess the pixel-level qualities of a
face image given an arbitrary face recognition network. To achieve this, a
model-specific quality value of the input image is estimated and used to build
a sample-specific quality regression model. Based on this model, quality-based
gradients are back-propagated and converted into pixel-level quality estimates.
In the experiments, we qualitatively and quantitatively investigated the
meaningfulness of our proposed pixel-level qualities based on real and
artificial disturbances and by comparing the explanation maps on faces
incompliant with the ICAO standards. In all scenarios, the results demonstrate
that the proposed solution produces meaningful pixel-level qualities enhancing
the interpretability of the complete face image quality. The code is publicly
available
