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8 February 2015 Comparative performance between human and automated face recognition systems, using CCTV imagery, different compression levels and scene parameters
A. Tsifouti, S. Triantaphillidou, M.-C. Larabi, Efthimia Bilissi, A. Psarrou
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Proceedings Volume 9396, Image Quality and System Performance XII; 93960M (2015) https://doi.org/10.1117/12.2083325
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
Abstract
In this investigation we identify relationships between human and automated face recognition systems with respect to compression. Further, we identify the most influential scene parameters on the performance of each recognition system. The work includes testing of the systems with compressed Closed-Circuit Television (CCTV) footage, consisting of quantified scene (footage) parameters. Parameters describe the content of scenes concerning camera to subject distance, facial angle, scene brightness, and spatio-temporal busyness. These parameters have been previously shown to affect the human visibility of useful facial information, but not much work has been carried out to assess the influence they have on automated recognition systems. In this investigation, the methodology previously employed in the human investigation is adopted, to assess performance of three different automated systems: Principal Component Analysis, Linear Discriminant Analysis, and Kernel Fisher Analysis. Results show that the automated systems are more tolerant to compression than humans. In automated systems, mixed brightness scenes were the most affected and low brightness scenes were the least affected by compression. In contrast for humans, low brightness scenes were the most affected and medium brightness scenes the least affected. Findings have the potential to broaden the methods used for testing imaging systems for security applications.
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A. Tsifouti, S. Triantaphillidou, M.-C. Larabi, Efthimia Bilissi, and A. Psarrou "Comparative performance between human and automated face recognition systems, using CCTV imagery, different compression levels and scene parameters", Proc. SPIE 9396, Image Quality and System Performance XII, 93960M (8 February 2015); https://doi.org/10.1117/12.2083325
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KEYWORDS
Data modeling
Facial recognition systems
Systems modeling
Cameras
Image quality
Principal component analysis
Imaging systems
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