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.
14th PErvasive Technologies Related to Assistive Environments Conference. virtual Conference (29.06.2021-02.07.2021)
doi: 10.1145/3453892.3458047
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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 %.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Rus, Silvia ; Nottebaum, Moritz ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Person Re-Identification in a Car Seat: Comparison of Cosine Similarity and Triplet Loss based approaches on Capacitive Proximity Sensing data |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Verlag: | ACM |
| Buchtitel: | PETRA '21: The 14th PErvasive Technologies Related to Assistive Environments Conference |
| Veranstaltungstitel: | 14th PErvasive Technologies Related to Assistive Environments Conference |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 29.06.2021-02.07.2021 |
| DOI: | 10.1145/3453892.3458047 |
| Kurzbeschreibung (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 %. |
| Freie Schlagworte: | Automatic identification system (AIS), Capacitive proximity sensing, Machine learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 10 Aug 2021 14:10 |
| Letzte Änderung: | 10 Aug 2021 14:10 |
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