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2022
Conference Paper
Titel
Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition
Abstract
Face Recognition (FR) is increasingly used in critical verification decisions and thus, there is a need for assessing the trustworthiness of such decisions. The confidence of a decision is often based on the overall performance of the model or on the image quality. We propose to propagate model uncertainties to scores and decisions in an effort to increase the transparency of verification decisions. This work presents two contributions. First, we propose an approach to estimate the uncertainty of face comparison scores. Second, we introduce a confidence measure of the system’s decision to provide insights into the verification decision. The suitability of the comparison scores uncertainties and the verification decision confidences have been experimentally proven on three face recognition models on two datasets.
Author(s)
Funder
Hessisches Ministerium für Wissenschaft und Kunst
Konferenz
Tags
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Branche: Information Technology
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Research Line: Computer vision (CV)
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Research Line: Machine learning (ML)
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LTA: Interactive decision-making support and assistance systems
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LTA: Machine intelligence, algorithms, and data structures (incl. semantics)
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Biometrics
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Face recognition
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Deep learning
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Machine learning
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ATHENE
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CRISP