Development of the Wits Face Database: an African database of high-resolution facial photographs and multimodal closedcircuit television (CCTV) recordings [version 1; peer review: 2 approved]

Date
2021
Authors
Bacci, Nicholas
Davimes, Joshua
Steyn, Maryna
Briers, Nanette
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Journal ISSN
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Abstract
Forensic facial comparison is a commonly used, yet under-evaluated method employed in medicolegal contexts across the world. Testing the accuracy and reliability of facial comparisons requires large scale controlled and matching facial image databases. Databases that contain images of individuals on closed-circuit television (CCTV), with matching formal and informal photographs are needed for this type of research. Although many databases are available, the majority if not all are developed in order to improve facial recognition and face detection algorithms through machine learning, with very limited if any measure of standardisation. This paper aims to review the available databases and describe the development of a high resolution, standardised facial photograph and CCTV recording database of male Africans. The database is composed of a total of 6220 standardised and uncontrolled suboptimal facial photographs of 622 matching individuals in five different views, as well as corresponding CCTV footage of 334 individuals recorded under different realistic conditions. A detailed description of the composition and acquisition process of the database as well as its subdivisions and possible uses are provided. The challenges and limitations of developing this database are also highlighted, particularly with regard to obtaining CCTV video recordings and ethics for a database of faces. The application process to access the database is also briefly described.
Description
Facial comparison is utilised by law enforcement to associate two sets of images, captured on video or photographically, to one another.
Keywords
Face database, CCTV, Facial photographs, Facial identification, Facial comparison
Citation
Chwistek M. Recent advances in understanding and managing cancer pain. F1000Res. 2017 Jun 20;6:945. doi: 10.12688/f1000research.10817.1.