Rus, Silvia ; Nottebaum, Moritz ; Kuijper, Arjan (2021):
Person Re-Identification in a Car Seat: Comparison of Cosine Similarity and Triplet Loss based approaches on Capacitive Proximity Sensing data.
In: PETRA '21: The 14th PErvasive Technologies Related to Assistive Environments Conference, pp. 97-104,
ACM, 14th PErvasive Technologies Related to Assistive Environments Conference, virtual Conference, 29.06.-02.07.2021, ISBN 978-1-4503-8792-7,
DOI: 10.1145/3453892.3458047,
[Conference or Workshop Item]

Abstract

Currently there is much research in the area of person identification. Mostly it is based on multi-biometric data. In this paper, we aim to leverage soft biometric properties to achieve person re-identification by using unobtrusive sensors, envisioning assistive environments, which recognize their user and thus automatically personalize and adapt. In practice, a car seat recognizes the person who sits down and greets the person with their own name, enabling various customisation in the car unique to the user, like seat configurations.We present a system composed of a sensor equipped car seat, which is able to recognize a person from a predefined group. We contribute two classification approaches based on cosine similarity measure and on triplet loss learning. These are thoroughly analysed and evaluated in a user study with nine participants. We achieve the best re-identification performance using a hand-crafted feature approach based on the comparing measure of cosine similarity combined with majority voting. The highest overall precision achieved in re-identifying a person from a group of nine users is 80 %.

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