A face biometric benchmarking review and characterisation

Mau, Sandra, Dadgostar, Farhad, Cullinan, Ian, Bigdeli, Abbas and Lovell, Brian C. (2011). A face biometric benchmarking review and characterisation. In: , IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain, (2120-2127). 6-13 November 2011.

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Author Mau, Sandra
Dadgostar, Farhad
Cullinan, Ian
Bigdeli, Abbas
Lovell, Brian C.
Title of paper A face biometric benchmarking review and characterisation
Conference name IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
Conference location Barcelona, Spain
Conference dates 6-13 November 2011
Proceedings title IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
Journal name Proceedings of the IEEE International Conference on Computer Vision
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2011
Sub-type Fully published paper
DOI 10.1109/ICCVW.2011.6130510
ISBN 9781467300629
Start page 2120
End page 2127
Total pages 8
Collection year 2012
Language eng
Abstract/Summary In order to advance face recognition research, algorithm performance has to be measured and compared using a range of metrics and operating characteristics. While public challenges such as the NIST-sponsored FERET, FRVT, FRGC, and MBGC are helpful to gauge comparative performance and improvement for a particular scenario, they typically are not sufficient to fully characterise the strengths and weaknesses of the face recognition algorithm, thus researchers need to do additionally benchmarking independently. This paper provides: (1) a detailed review and categorisation of publicly available face biometrics benchmarks; (2) a discussion of metrics and performance factors to consider; (3) a proposal for a meta-face biometric benchmarking regime which suggests guidelines for benchmarking across multiple datasets to more fully characterise and quantify face recognition performance across various operating characteristics; and (4) a sample demonstration which compare the performance of a face recognition algorithm before and after inclusion of a face quality metric.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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