Damer, Naser ; Grebe, Jonas Henry ; Zienert, Steffen ; Kirchbuchner, Florian ; Kuijper, Arjan (2019)
On the Generalization of Detecting Face Morphing Attacks as Anomalies: Novelty vs. Outlier Detection.
10th International Conference on Biometrics Theory, Applications and Systems (BTAS 2019). Tampa, USA (23.-26.09.)
doi: 10.1109/BTAS46853.2019.9185995
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Face morphing attacks are verifiable to multiple identities, leading to faulty identity links. Recent works have studied the face morphing attack detection performance over variations in morphing approaches, pointing out low generalization. This work studies detecting these attacks as anomalies and discusses the performance and generalization over different morphing types. We also analyze the accuracy and generalization effect of including different amounts of attack contamination in the anomaly training data (novelty vs. outlier). This is performed with two baseline 2-class classifiers, two approaches for anomaly detection, two image feature extractions, two morphing types, and variations in contamination levels and tolerated training errors. The results points out the relative lower performance, but higher generalization ability, of anomaly detection in comparison to 2-class classifiers, along with the benefits of contaminating the anomaly training data.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Damer, Naser ; Grebe, Jonas Henry ; Zienert, Steffen ; Kirchbuchner, Florian ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | On the Generalization of Detecting Face Morphing Attacks as Anomalies: Novelty vs. Outlier Detection |
| Sprache: | Englisch |
| Publikationsjahr: | 3 September 2019 |
| Verlag: | IEEE |
| Veranstaltungstitel: | 10th International Conference on Biometrics Theory, Applications and Systems (BTAS 2019) |
| Veranstaltungsort: | Tampa, USA |
| Veranstaltungsdatum: | 23.-26.09. |
| DOI: | 10.1109/BTAS46853.2019.9185995 |
| URL / URN: | https://doi.org/10.1109/BTAS46853.2019.9185995 |
| Kurzbeschreibung (Abstract): | Face morphing attacks are verifiable to multiple identities, leading to faulty identity links. Recent works have studied the face morphing attack detection performance over variations in morphing approaches, pointing out low generalization. This work studies detecting these attacks as anomalies and discusses the performance and generalization over different morphing types. We also analyze the accuracy and generalization effect of including different amounts of attack contamination in the anomaly training data (novelty vs. outlier). This is performed with two baseline 2-class classifiers, two approaches for anomaly detection, two image feature extractions, two morphing types, and variations in contamination levels and tolerated training errors. The results points out the relative lower performance, but higher generalization ability, of anomaly detection in comparison to 2-class classifiers, along with the benefits of contaminating the anomaly training data. |
| Freie Schlagworte: | Biometrics, Machine learning, Face recognition, Spoofing attacks |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 22 Sep 2020 13:28 |
| Letzte Änderung: | 22 Sep 2020 13:28 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum