Huber, Marco ; Terhörst, Philipp ; Kirchbuchner, Florian ; Kuijper, Arjan ; Damer, Naser (2023)
Uncertainty-aware Comparison Scores for Face Recognition.
11th International Workshop on Biometrics and Forensics. Barcelona, Spain (19.-20.04.2023)
doi: 10.1109/IWBF57495.2023.10157282
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Estimating and understanding uncertainty in face recognition systems is receiving increasing attention as face recognition systems spread worldwide and process privacy and security-related data. In this work, we investigate how such uncertainties can be further utilized to increase the accuracy and therefore the trust of automatic face recognition systems. We propose to use the uncertainties of extracted face features to compute a new uncertainty-aware comparison score (UACS). This score takes into account the estimated uncertainty during the calculation of the comparison score, leading to a reduction in verification errors. To achieve this, we model the comparison score and its uncertainty as a probability distribution and measure its distance to a distribution of an ideal genuine comparison. In extended experiments with three face recognition models and on six benchmarks, we investigated the impact of our approach and demonstrated its benefits in enhancing the verification performance and the genuine-imposter comparison scores separability.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Huber, Marco ; Terhörst, Philipp ; Kirchbuchner, Florian ; Kuijper, Arjan ; Damer, Naser |
| Art des Eintrags: | Bibliographie |
| Titel: | Uncertainty-aware Comparison Scores for Face Recognition |
| Sprache: | Englisch |
| Publikationsjahr: | 23 Juni 2023 |
| Verlag: | IEEE |
| Buchtitel: | 2023 11th International Workshop on Biometrics and Forensics (IWBF) |
| Veranstaltungstitel: | 11th International Workshop on Biometrics and Forensics |
| Veranstaltungsort: | Barcelona, Spain |
| Veranstaltungsdatum: | 19.-20.04.2023 |
| DOI: | 10.1109/IWBF57495.2023.10157282 |
| Kurzbeschreibung (Abstract): | Estimating and understanding uncertainty in face recognition systems is receiving increasing attention as face recognition systems spread worldwide and process privacy and security-related data. In this work, we investigate how such uncertainties can be further utilized to increase the accuracy and therefore the trust of automatic face recognition systems. We propose to use the uncertainties of extracted face features to compute a new uncertainty-aware comparison score (UACS). This score takes into account the estimated uncertainty during the calculation of the comparison score, leading to a reduction in verification errors. To achieve this, we model the comparison score and its uncertainty as a probability distribution and measure its distance to a distribution of an ideal genuine comparison. In extended experiments with three face recognition models and on six benchmarks, we investigated the impact of our approach and demonstrated its benefits in enhancing the verification performance and the genuine-imposter comparison scores separability. |
| Freie Schlagworte: | Biometrics, Face recognition, Machine learning, Deep learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 19 Jul 2023 07:20 |
| Letzte Änderung: | 19 Jul 2023 07:20 |
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