The objective of this work is set-based face recognition, i.e. to decide if
two sets of images of a face are of the same person or not. Conventionally, the
set-wise feature descriptor is computed as an average of the descriptors from
individual face images within the set. In this paper, we design a neural
network architecture that learns to aggregate based on both "visual" quality
(resolution, illumination), and "content" quality (relative importance for
discriminative classification). To this end, we propose a Multicolumn Network
(MN) that takes a set of images (the number in the set can vary) as input, and
learns to compute a fix-sized feature descriptor for the entire set. To
encourage high-quality representations, each individual input image is first
weighted by its "visual" quality, determined by a self-quality assessment
module, and followed by a dynamic recalibration based on "content" qualities
relative to the other images within the set. Both of these qualities are learnt
implicitly during training for set-wise classification. Comparing with the
previous state-of-the-art architectures trained with the same dataset
(VGGFace2), our Multicolumn Networks show an improvement of between 2-6% on the
IARPA IJB face recognition benchmarks, and exceed the state of the art for all
methods on these benchmarks.Comment: To appear in BMVC201