On the Use of Discriminative Cohort Score Normalization for Unconstrained Face Recognition

Facial imaging has been largely addressed for
automatic personal identification, in a variety of different
environments. However, automatic face recognition becomes
very challenging whenever the acquisition conditions are unconstrained.
In this paper, a picture-specific cohort normalization
approach, based on polynomial regression, is proposed to enhance
the robustness of face matching under challenging conditions.
A careful analysis is presented to better understand the actual
discriminative power of a given cohort set. In particular, it is
shown that the cohort polynomial regression alone conveys some
discriminative information on the matching face pair, which
is just marginally worse than the raw matching score. The
influence of the cohort set size in the matching accuracy is also
investigated. Further, tests performed on the Face Recognition
Grand Challenge ver 2 database and the labeled faces in the wild
database allowed to determine the relation between the quality
of the cohort samples and cohort normalization performance.
Experimental results obtained from the LFW data set demonstrate
the effectiveness of the proposed approach to improve the
recognition accuracy in unconstrained face acquisition scenarios.