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Auflistung nach Schlagwort "Trustworthiness and explainability"

1 - 2 von 2
  • Konferenzbeitrag
    Face verification explainability heatmap generation using a vision transformer
    (BIOSIG 2023, 2023) Ricardo Correia, Paulo L Correia
    Explainable Face Recognition (XFR) is a critical technology to support the large deployment of learning-based face recognition solutions. This paper aims at contributing to the more transparent usage of Vision Transformers (ViTs) for face verification (FV) tasks, by proposing a novel approach for generating FV explainability heatmaps, for both positive and negative decisions. The proposed solution leverages on the attention maps generated by a ViT and employs masking techniques to create masks based on the highlighted regions in the attention maps. These masks are applied to the pair of faces, and the masking technique with most impact on the decision is selected to be used to generate heatmaps for the probe-gallery pair of faces. These heatmaps offer valuable insights into the decision-making process, shedding light on the most important face regions for the verification outcome. The key novelty of this paper lies in the proposed approach for generating explainability heatmaps tailored for verification pairs in the context of ViT models, which combines the ViT attention maps regions of the probe-gallery pair to create masks that allow evaluating those region´s impact on the verification decision for both positive and negative decisions.
  • Konferenzbeitrag
    A RISE-based explainability method for genuine and impostor face verification
    (BIOSIG 2023, 2023) Naima Bousnina, Joao Ascenso
    Heat Map (HM)-based explainable Face Verification (FV) has the goal to visually interpret the decision-making of black-box FV models. Despite the impressive results, state-of-the-art FV explainability methods based on HMs mainly address genuine verification by generating visual explanations that reveal the similar face regions which most contributed for acceptance decisions. However, the similar face regions may not be the unique critical regions for the model decision, notably when rejection decisions are performed. To address this issue, this paper proposes a more complete FV explainability method, providing meaningful HM-based explanations for both genuine and impostor verification and associated acceptance and rejection decisions. The proposed method adapts the RISE algorithm for FV to generate Similarity Heat Maps (S-HMs) and Dissimilarity Heat Maps (D-HMs) which offer reliable explanations to all types of FV decisions. Qualitative and quantitative experimental results show the effectiveness of the proposed FV explainability method beyond state-of-the-art benchmarks.