VBOLO: Video Be On the Look Out
Person re-identification (ReID) is a popular topic of research. Almost all existing ReID approaches employ local and global body features (e.g., clothing color and pattern, body symmetry, etc.). These `body ReID' methods implicitly assume that facial resolution is too low to aid in the ReID process. This project attempts to explore and show that faces, even when captured in low resolution environments, may contain unique and stable features for ReID. We contribute a new facial ReID dataset that was collected from a real surveillance network in a municipal rapid transit system. It is a challenging ReID dataset, as it includes intentional changes in persons' appearances over time. We conduct multiple experiments on this dataset, exploiting deep neural networks combined with metric learning in an end-to-end fashion.
Pei Li, Joel Brogan, Domingo Mery, Patrick Flynn
Collaborators: Loreto Prieto
Learning Face Similarity for Re-Identification from Real Surveillance Video: A Deep Metric Solution, Pei Li, Maria Loreto Prieto, Patrick J. Flynn, Domingo Mery, February 2018: [pdf]
Toward Facial Re-Identification: Experiments with Data from an Operational Surveillance Camera Plant, Pei Li, Joel Brogan, Patrick J. Flynn, IEEE, December 2016: [pdf]
