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Summary of Bridging Human Concepts and Computer Vision For Explainable Face Verification, by Miriam Doh (umons et al.


Bridging Human Concepts and Computer Vision for Explainable Face Verification

by Miriam Doh, Caroline Mazini Rodrigues, Nicolas Boutry, Laurent Najman, Matei Mancas, Hugues Bersini

First submitted to arxiv on: 30 Jan 2024

Categories

  • Main: Computer Vision and Pattern Recognition (cs.CV)
  • Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper proposes an innovative approach to explainable artificial intelligence (XAI) in face verification systems. By combining computer and human vision, the authors aim to increase the interpretability of AI decisions. Specifically, they draw inspiration from the human perceptual process to analyze how machines perceive facial regions during comparison tasks. The proposed method uses Mediapipe’s segmentation technique to identify distinct human-semantic areas on faces. Furthermore, two model-agnostic algorithms are adapted to provide insights into the decision-making processes.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper helps us understand how artificial intelligence makes decisions in face verification systems. It wants to make AI more transparent and fair by showing humans why it chose one person’s face over another. The authors take a unique approach by combining human and computer vision to see how machines look at faces. They use special software called Mediapipe that can identify different parts of the face, like eyes or nose. This helps us understand what AI is looking for when it compares two faces. The goal is to make AI more trustworthy by making its decisions easier to understand.

Keywords

* Artificial intelligence