  Face recognition from image sets acquired under unregulated and uncontrolled
settings, such as at large distances, low resolutions, varying viewpoints,
illumination, pose, and atmospheric conditions, is challenging. Face feature
aggregation, which involves aggregating a set of N feature representations
present in a template into a single global representation, plays a pivotal role
in such recognition systems. Existing works in traditional face feature
aggregation either utilize metadata or high-dimensional intermediate feature
representations to estimate feature quality for aggregation. However,
generating high-quality metadata or style information is not feasible for
extremely low-resolution faces captured in long-range and high altitude
settings. To overcome these limitations, we propose a feature distribution
conditioning approach called CoNAN for template aggregation. Specifically, our
method aims to learn a context vector conditioned over the distribution
information of the incoming feature set, which is utilized to weigh the
features based on their estimated informativeness. The proposed method produces
state-of-the-art results on long-range unconstrained face recognition datasets
such as BTS, and DroneSURF, validating the advantages of such an aggregation
strategy.
