  Deep learning advanced face recognition to an unprecedented accuracy.
However, understanding how local parts of the face affect the overall
recognition performance is still mostly unclear. Among others, face swap has
been experimented to this end, but just for the entire face. In this paper, we
propose to swap facial parts as a way to disentangle the recognition relevance
of different face parts, like eyes, nose and mouth. In our method, swapping
parts from a source face to a target one is performed by fitting a 3D prior,
which establishes dense pixels correspondence between parts, while also
handling pose differences. Seamless cloning is then used to obtain smooth
transitions between the mapped source regions and the shape and skin tone of
the target face. We devised an experimental protocol that allowed us to draw
some preliminary conclusions when the swapped images are classified by deep
networks, indicating a prominence of the eyes and eyebrows region. Code
available at https://github.com/clferrari/FacePartsSwap
