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

Moritz Nottebaum, Silvia Faquiri, Arjan Kuijper

Abstract:

Currently there is much research in the area of person identifi-
cation. 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 per-
son 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 com-
bined with majority voting. The highest overall precision achieved
in re-identifying a person from a group of nine users is 80 %.