  For a given identity in a face dataset, there are certain iconic images which
are more representative of the subject than others. In this paper, we explore
the problem of computing the iconicity of a face. The premise of the proposed
approach is as follows: For an identity containing a mixture of iconic and non
iconic images, if a given face cannot be successfully matched with any other
face of the same identity, then the iconicity of the face image is low. Using
this information, we train a Siamese Multi-Layer Perceptron network, such that
each of its twins predict iconicity scores of the image feature pair, fed in as
input. We observe the variation of the obtained scores with respect to
covariates such as blur, yaw, pitch, roll and occlusion to demonstrate that
they effectively predict the quality of the image and compare it with other
existing metrics. Furthermore, we use these scores to weight features for
template-based face verification and compare it with media averaging of
features.
