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Person Re-Identification in a Car Seat: Comparison of Cosine Similarity and Triplet Loss based approaches on Capacitive Proximity Sensing data

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 %.

Item Type: Conference or Workshop Item
Erschienen: 2021
Creators: Rus, Silvia ; Nottebaum, Moritz ; Kuijper, Arjan
Title: Person Re-Identification in a Car Seat: Comparison of Cosine Similarity and Triplet Loss based approaches on Capacitive Proximity Sensing data
Language: English
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 %.

Title of Book: PETRA '21: The 14th PErvasive Technologies Related to Assistive Environments Conference
Publisher: ACM
ISBN: 978-1-4503-8792-7
Uncontrolled Keywords: Automatic identification system (AIS), Capacitive proximity sensing, Machine learning
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 14th PErvasive Technologies Related to Assistive Environments Conference
Event Location: virtual Conference
Event Dates: 29.06.-02.07.2021
Date Deposited: 10 Aug 2021 14:10
DOI: 10.1145/3453892.3458047
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