Abstract. The objective of this work is set-based verification, e.g. to
decide if two sets of images of a face are of the same person or not. The
traditional approach to this problem is to learn to generate a feature
vector per image, aggregate them into one vector to represent the set,
and then compute the cosine similarity between sets. Instead, we design a
neural network architecture that can directly learn set-wise verification.
Our contributions are: (i) We propose a Deep Comparator Network (DCN)
that can ingest a pair of sets (each may contain a variable number of images) as inputs, and compute a similarity between the pair – this involves
attending to multiple discriminative local regions (landmarks), and comparing local descriptors between pairs of faces; (ii) To encourage highquality representations for each set, internal competition is introduced
for recalibration based on the landmark score; (iii) Inspired by image
retrieval, a novel hard sample mining regime is proposed to control the
sampling process, such that the DCN is complementary to the standard
image classification models. Evaluations on the IARPA Janus face recognition benchmarks show that the comparator networks outperform the
previous state-of-the-art results by a large margin