Learning face similarity for re-identification from real surveillance video: A deep metric solution

Abstract

Person re-identification (ReID) is the task of automatically matching persons across surveillance cameras with location or time differences. Nearly all proposed ReID approaches exploit body features. Even if successfully captured in the scene, faces are often assumed to be unhelpful to the ReID process[3]. As cameras and surveillance systems improve, ‘Facial ReID’ approaches deserve attention. The following contributions are made in this work: 1) We describe a high-quality dataset for person re-identification featuring faces. This dataset was collected from a real surveillance network in a municipal rapid transit system, and includes the same people appearing in multiple sites at multiple times wearing different attire. 2) We employ new DNN architectures and patch matching techniques to handle face misalignment in quality regimes where landmarking fails. We further boost the performance by adopting the fully convolutional structure and spatial pyramid pooling (SPP).