Siegmund, Dirk ; Tran, Vinh Phuc ; Wilmsdorff, Julian von ; Kirchbuchner, Florian ; Kuijper, Arjan (2019)
Piggybacking Detection Based on Coupled Body-Feet Recognition at Entrance Control.
CIARP 2019 : 24th Iberoamerican Congress on Pattern Recognition. Havana, Cuba (28.10.2019-31.10.2019)
doi: 10.1007/978-3-030-33904-3_74
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
A major risk of an automated high-security entrance control is that an authorized person takes an unauthorized person into the secured area. This practice is called “piggybacking”. Known systems try to prevent it by using physical barriers combined with sensory or camera based algorithms. In this paper we present a multi-sensor solution for verifying the number of persons that stand within a defined transit area. We use sensors that are installed in the floor to detect feet as well as camera shots taken from above. We propose an image-based approach that uses change detection to extract motion from a sequence of images and classify it by using a convolutional neural network. Our sensor-based approach shows how user interactions can be used to facilitate safe separation. Both methods are computationally efficient so they can be used in embedded systems. In the evaluation, we were able to achieve state-ofthe- art results for both approaches individually. Merging both methods sustainably prevents piggybacking, at a BPCER of 7.1%, where bona fide presentations are incorrectly classified as presentation attacks.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Siegmund, Dirk ; Tran, Vinh Phuc ; Wilmsdorff, Julian von ; Kirchbuchner, Florian ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Piggybacking Detection Based on Coupled Body-Feet Recognition at Entrance Control |
| Sprache: | Englisch |
| Publikationsjahr: | 2019 |
| Veranstaltungstitel: | CIARP 2019 : 24th Iberoamerican Congress on Pattern Recognition |
| Veranstaltungsort: | Havana, Cuba |
| Veranstaltungsdatum: | 28.10.2019-31.10.2019 |
| DOI: | 10.1007/978-3-030-33904-3_74 |
| URL / URN: | https://link.springer.com/book/10.1007/978-3-030-33904-3 |
| Kurzbeschreibung (Abstract): | A major risk of an automated high-security entrance control is that an authorized person takes an unauthorized person into the secured area. This practice is called “piggybacking”. Known systems try to prevent it by using physical barriers combined with sensory or camera based algorithms. In this paper we present a multi-sensor solution for verifying the number of persons that stand within a defined transit area. We use sensors that are installed in the floor to detect feet as well as camera shots taken from above. We propose an image-based approach that uses change detection to extract motion from a sequence of images and classify it by using a convolutional neural network. Our sensor-based approach shows how user interactions can be used to facilitate safe separation. Both methods are computationally efficient so they can be used in embedded systems. In the evaluation, we were able to achieve state-ofthe- art results for both approaches individually. Merging both methods sustainably prevents piggybacking, at a BPCER of 7.1%, where bona fide presentations are incorrectly classified as presentation attacks. |
| Freie Schlagworte: | Computer vision Human-computer interaction (HCI) Research and development Access control |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 17 Apr 2020 09:44 |
| Letzte Änderung: | 28 Jul 2021 11:42 |
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