Comparator Networks
Weidi Xie, Li Shen, Andrew Zisserman

TL;DR
This paper introduces Deep Comparator Networks (DCN), a novel neural architecture for set-based face verification that directly learns to compare sets of images by attending to local regions, outperforming previous methods.
Contribution
The paper presents a new neural network architecture that directly compares sets of images for verification, incorporating local region attention and a novel hard sample mining regime.
Findings
DCN outperforms previous state-of-the-art on Janus face recognition benchmarks.
The model effectively attends to discriminative local regions for set comparison.
Hard sample mining improves the model's robustness and accuracy.
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 high-quality representations for each set, internal competition is introduced for recalibration based on the landmark…
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Taxonomy
TopicsFace recognition and analysis · Face and Expression Recognition · Biometric Identification and Security
