CoNAN: Conditional Neural Aggregation Network For Unconstrained Face Feature Fusion

TL;DR

CoNAN introduces a novel feature distribution conditioning method for aggregating face features in unconstrained settings, achieving state-of-the-art results on challenging long-range face recognition datasets.

Contribution

The paper proposes CoNAN, a new neural aggregation network that conditions on feature distribution to improve face recognition in uncontrolled environments.

Findings

Achieves state-of-the-art performance on BTS and DroneSURF datasets.

Effectively handles low-resolution and long-range face images.

Outperforms existing feature aggregation methods.

Abstract

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.