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Deep learning advanced face recognition to an unprecedented accuracy.However, understanding how local parts of the face affect the overallrecognition performance is still mostly unclear. Among others, face swap hasbeen experimented to this end, but just for the entire face. In this paper, wepropose to swap facial parts as a way to disentangle the recognition relevanceof different face parts, like eyes, nose and mouth. In our method, swappingparts from a source face to a target one is performed by fitting a 3D prior,which establishes dense pixels correspondence between parts, while alsohandling pose differences. Seamless cloning is then used to obtain smoothtransitions between the mapped source regions and the shape and skin tone ofthe target face. We devised an experimental protocol that allowed us to drawsome preliminary conclusions when the swapped images are classified by deepnetworks, indicating a prominence of the eyes and eyebrows region. Codeavailable at https://github.com/clferrari/FacePartsSwap

What makes you, you? Analyzing Recognition by Swapping Face Parts