Bridging Human Concepts and Computer Vision for Explainable Face
  Verification
Miriam Doh Caroline Mazini Rodrigues Nicolas Boutry Laurent Najman Matei Mancas Hugues Bersini
With Artificial Intelligence (AI) influencing the decision-making process of
sensitive applications such as Face Verification, it is fundamental to ensure
the transparency, fairness, and accountability of decisions. Although
Explainable Artificial Intelligence (XAI) techniques exist to clarify AI
decisions, it is equally important to provide interpretability of these
decisions to humans. In this paper, we present an approach to combine computer
and human vision to increase the explanation's interpretability of a face
verification algorithm. In particular, we are inspired by the human perceptual
process to understand how machines perceive face's human-semantic areas during
face comparison tasks. We use Mediapipe, which provides a segmentation
technique that identifies distinct human-semantic facial regions, enabling the
machine's perception analysis. Additionally, we adapted two model-agnostic
algorithms to provide human-interpretable insights into the decision-making
processes.