Bridging Human Concepts and Computer Vision for Explainable Face Verification
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917613641138176 |
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| author | Doh, Miriam Rodrigues, Caroline Mazini Boutry, Nicolas Najman, Laurent Mancas, Matei Bersini, Hugues |
| author_facet | Doh, Miriam Rodrigues, Caroline Mazini Boutry, Nicolas Najman, Laurent Mancas, Matei Bersini, Hugues |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_08789 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bridging Human Concepts and Computer Vision for Explainable Face Verification Doh, Miriam Rodrigues, Caroline Mazini Boutry, Nicolas Najman, Laurent Mancas, Matei Bersini, Hugues Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning 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. |
| title | Bridging Human Concepts and Computer Vision for Explainable Face Verification |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2403.08789 |