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

TL;DR

This paper investigates how different facial parts contribute to face recognition by swapping parts like eyes, nose, and mouth using a 3D prior and seamless cloning, revealing the prominence of eyes and eyebrows.

Contribution

It introduces a novel face part swapping method using 3D fitting and seamless cloning to analyze recognition relevance of facial regions.

Findings

Eyes and eyebrows are most influential in recognition.

Swapped images help understand local face part importance.

Method enables controlled face part manipulation for analysis.

Abstract

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