Person Re-identification with Hyperspectral Multi-Camera Systems --- A Pilot Study
This is an incremental study that addresses person re-identification for surveillance systems by exploring hyperspectral data as a potential improvement over traditional color images.
The paper tackled person re-identification by proposing the use of hyperspectral imagery to capture unique spectral signatures from skin, which substantially enhanced performance compared to color images in a pilot study with 15 people.
Person re-identification in a multi-camera environment is an important part of modern surveillance systems. Person re-identification from color images has been the focus of much active research, due to the numerous challenges posed with such analysis tasks, such as variations in illumination, pose and viewpoints. In this paper, we suggest that hyperspectral imagery has the potential to provide unique information that is expected to be beneficial for the re-identification task. Specifically, we assert that by accurately characterizing the unique spectral signature for each person's skin, hyperspectral imagery can provide very useful descriptors (e.g. spectral signatures from skin pixels) for re-identification. Towards this end, we acquired proof-of-concept hyperspectral re-identification data under challenging (practical) conditions from 15 people. Our results indicate that hyperspectral data result in a substantially enhanced re-identification performance compared to color (RGB) images, when using spectral signatures over skin as the feature descriptor.