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

: Siegmund, Dirk; Tran, Vinh Phuc; Wilmsdorff, Julian von; Kirchbuchner, Florian; Kuijper, Arjan


Nyström, Ingela (Ed.):
Progress in patteren recognition, image analysis, computer vision, and applications. 24th Iberoamerican congress, CIARP 2019. Proceedings : Havana, Cuba, October 28-31, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11896)
ISBN: 978-3-030-33903-6 (Print)
ISBN: 978-3-030-33904-3 (Online)
ISBN: 3-030-33903-3
Iberoamerican Congress on Pattern Recognition (CIARP) <24, 2019, Havanna>
Fraunhofer IGD ()
Lead Topic: Smart City; Research Line: Computer vision (CV); Research Line: Human computer interaction (HCI); computer vision; Human-computer interaction (HCI); research and development; access control

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-of-the-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.