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Assessment of Facial Recognition System Performance in Realistic Operating Environments

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Face Recognition Across the Imaging Spectrum

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Abstract

An end-to-end facial recognition system performance depends on a variety of factors. The optical system, environment, illumination, target, and recognition algorithm can all affect its accuracy. Typically, only the facial recognition algorithm has been considered when evaluating performance. The remaining environmental and system components have not been considered in the design of facial recognition imaging systems. However, in scenarios relevant to the military and homeland security, the effects of weather and range can severely degrade performance and it is necessary to understand the conditions where this happens. This work introduces a methodology to explore the sensitivities of a facial recognition imaging system to blur, noise, and turbulence effects. Using a government-owned and an open source facial recognition algorithm, system performance is evaluated under different optical blurs , sensor noises , and turbulence conditions . The ramifications of these results on the design of long-range facial recognition systems are also discussed.

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Authors and Affiliations

  1. U.S. Army RDECOM CERDEC Night Vision Electronic Sensors Directorate, RDER-NVM, 10221 Burbeck Rd, Fort Belvoir, VA, 22060, USA

    Kevin R. Leonard

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  1. Kevin R. Leonard
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Correspondence to Kevin R. Leonard .

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Editors and Affiliations

  1. Multispectral Imagery Lab—MILab, LCSEE, West Virginia University, MORGANTOWN, West Virginia, USA

    Thirimachos Bourlai

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Leonard, K.R. (2016). Assessment of Facial Recognition System Performance in Realistic Operating Environments. In: Bourlai, T. (eds) Face Recognition Across the Imaging Spectrum. Springer, Cham. https://doi.org/10.1007/978-3-319-28501-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-28501-6_6

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