Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data.

ABSTRACT: Walking gait data acquired with force platforms may be used for person re-identification (re-ID) in various authentication, surveillance, and forensics applications. Current force platform-based re-ID systems classify a fixed set of identities (IDs), which presents a problem when IDs are added or removed from the database. We formulated force platform-based re-ID as a deep metric learning (DML) task, whereby a deep neural network learns a feature representation that can be compared between inputs using a distance metric. The force platform dataset used in this study is one of the largest and the most comprehensive of its kind, containing 193 IDs with significant variations in clothing, footwear, walking speed, and time between trials. Several DML model architectures were evaluated in a cha

SUBMITTER: Duncanson KA 

PROVIDER: S-EPMC10099366 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature