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Face Recognition Performance: Role of Demographic Information
January 2012
Brendan F. Klare, The MITRE Corporation
Mark J. Burge, The MITRE Corporation
Joshua C. Klontz, The MITRE Corporation
Richard W. Vorder Bruegge, The MITRE Corporation
Anil K. Jain, The MITRE Corporation
ABSTRACT
This paper studies the influence of demographics on
the performance of face recognition algorithms. The recognition
accuracies of six different face recognition algorithms (three
commercial, two non-trainable, and one trainable) are computed
on a large scale gallery that is partitioned so that each partition
consists entirely of specific demographic cohorts. Eight
total cohorts are isolated based on gender (male and female),
race/ethnicity (Black, White, and Hispanic), and age group (18 to
30, 30 to 50, and 50 to 70 years old). Experimental results demonstrate
that both commercial and the non-trainable algorithms
consistently have lower matching accuracies on the same cohorts
(females, Blacks, and age group 18 to 30). Additional experiments
investigate the impact of the demographic distribution in the
training set on the performance of a trainable face recognition
algorithm. We show that the matching accuracy for race/ethnicity
and age cohorts can be improved by training exclusively on that
specific cohort. Operationally, this leads to a scenario, called
dynamic face matcher selection, where multiple face recognition
algorithms (each trained on different demographic cohorts), are
available for a biometric system operator to select based on the
demographic information extracted from a probe image. This
procedure should lead to improved face recognition accuracy in
many intelligence and law enforcement face recognition scenarios.
Additional Search Keywords
face recognition, demographics, race/ethnicity, gender, age, training, dynamic face matcher selection
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