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Piggybacking Detection Based on Coupled Body-Feet Recognition at Entrance Control

Siegmund, Dirk and Tran, Vinh Phuc and von Wilmsdorff, Julian and Kirchbuchner, Florian and Kuijper, Arjan (2019):
Piggybacking Detection Based on Coupled Body-Feet Recognition at Entrance Control.
pp. 780-789, CIARP 2019 : 24th Iberoamerican Congress on Pattern Recognition, Havana, Cuba, Oct 28. - Oct 31., 2019, ISSN 0302-9743,
DOI: 10.1007/978-3-030-33904-3_74,
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Siegmund, Dirk and Tran, Vinh Phuc and von Wilmsdorff, Julian and Kirchbuchner, Florian and Kuijper, Arjan
Title: Piggybacking Detection Based on Coupled Body-Feet Recognition at Entrance Control
Language: English
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.

Uncontrolled Keywords: Computer vision Human-computer interaction (HCI) Research and development Access control
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: CIARP 2019 : 24th Iberoamerican Congress on Pattern Recognition
Event Location: Havana, Cuba
Event Dates: Oct 28. - Oct 31., 2019
Date Deposited: 17 Apr 2020 09:44
DOI: 10.1007/978-3-030-33904-3_74
Official URL: https://link.springer.com/book/10.1007/978-3-030-33904-3
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