Huber, Marco ; Terhörst, Philipp ; Kirchbuchner, Florian ; Damer, Naser ; Kuijper, Arjan (2022)
Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition.
33rd British Machine Vision Conference 2022. London, United Kingdom (21.11.2022-24.11.2022)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Huber, Marco ; Terhörst, Philipp ; Kirchbuchner, Florian ; Damer, Naser ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition |
| Sprache: | Englisch |
| Publikationsjahr: | 25 November 2022 |
| Verlag: | BMVA Press |
| Buchtitel: | BMVC 2022: 33rd British Machine Vision Conference Proceedings. Online resource |
| Veranstaltungstitel: | 33rd British Machine Vision Conference 2022 |
| Veranstaltungsort: | London, United Kingdom |
| Veranstaltungsdatum: | 21.11.2022-24.11.2022 |
| URL / URN: | https://bmvc2022.mpi-inf.mpg.de/506/ |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Biometrics, Face recognition, Deep learning, Machine learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 28 Mär 2023 12:58 |
| Letzte Änderung: | 28 Mär 2023 12:58 |
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