# Attentional Feature-Pair Relation Networks for Accurate Face Recognition

## TL;DR

This paper introduces AFRN, a novel face recognition method that leverages attentional feature-pair relations and top-K selection to improve accuracy under challenging conditions like pose, expression, and illumination variations.

## Contribution

The paper proposes a new face recognition approach using attentional feature-pair relations with top-K selection, enhancing robustness against facial variations.

## Key findings

- Achieves state-of-the-art performance on multiple face verification datasets.
- Effectively handles pose, expression, and illumination changes.
- Outperforms existing methods in accuracy on benchmark datasets.

## Abstract

Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and illumination is a big challenging problem for its practical application. Such variations make face recognition more difficult. In this paper, we propose a novel face recognition method, called Attentional Feature-pair Relation Network (AFRN), which represents the face by the relevant pairs of local appearance block features with their attention scores. The AFRN represents the face by all possible pairs of the 9x9 local appearance block features, the importance of each pair is considered by the attention map that is obtained from the low-rank bilinear pooling, and each pair is weighted by its corresponding attention score. To increase the accuracy, we select top-K pairs of local appearance block features as relevant facial information and drop the remaining irrelevant. The weighted top-K pairs are propagated to extract the joint feature-pair relation by using bilinear attention network. In experiments, we show the effectiveness of the proposed AFRN and achieve the outstanding performance in the 1:1 face verification and 1:N face identification tasks compared to existing state-of-the-art methods on the challenging LFW, YTF, CALFW, CPLFW, CFP, AgeDB, IJB-A, IJB-B, and IJB-C datasets.