HBRC-500: A Long Range Recognition Benchmark Dataset using Face and Whole-body Imagery

• Published in: 2023 IEEE International Joint Conference on Biometrics (IJCB) pp. 1 - 11
• Main Authors: Fondje, Cedric Nimpa, Nikhal, Kshitij, Peace, John Brennan, Karl, Ryan, Lee, Mun Wai, Berkowitz, Phillip, Gramzinski, Katrina, Kennedy, Bridget, Uzuegbunam, Nkirukaegbunam, Ou, Victoria, Barret, Tyler, Arend, Oliver, Ming, Wei, Semenova, Svetlana, Riggan, Benjamin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25.09.2023
• Subjects: Cameras; Face recognition; Image recognition; Probes; Research and development; Soft sensors; Surveillance
• ISSN: 2474-9699
• DOI: 10.1109/IJCB57857.2023.10448770

While biometric-face and whole-body-recognition technology have recently advanced and matured, there are increasing interests in enhanced long-range recognition capabilities. However, long-range recognition requires the use of large, specialized datasets to support research and development for next generation systems. Moreover, existing datasets are further limited by the types of modalities (face or whole-body), number of subjects, maximum standoff distance, clothing variability, or availability restrictions. For long-range recognition, low-quality probe (query) images, which are often acquired from extended standoff distances or aerial platforms, are matched against higher quality gallery images and frequently results in poor identification performance. To address the growing needs for relevant data sources, a large-scale biometric dataset was collected and curated for long-range biometric recognition. This dataset is comprised of more than 1.2 million outdoor and 250,000 indoor (face and whole-body) images from more than 250 subjects that were acquired using various high-end cameras, including Canon and Nikon DSLR cameras, surveillance cameras, specialized long-range face cameras, and UAV platforms. The primary goal of this dataset is to support the development of algorithms for face and whole-body recognition at extended standoff distances. The availability of such a dataset is crucial in advancing technology for recognition under challenging conditions such as atmospheric turbulence.