Neural Aggregation Network for Video Face Recognition

TL;DR

This paper introduces a Neural Aggregation Network that effectively combines variable face images from videos into a fixed feature for recognition, improving accuracy without extra supervision.

Contribution

The proposed NAN uses attention-based aggregation to automatically emphasize high-quality face images, achieving state-of-the-art video face recognition performance.

Findings

Outperforms naive aggregation methods on benchmarks

Automatically learns to prioritize high-quality images

Achieves state-of-the-art accuracy on multiple datasets

Abstract

This paper presents a Neural Aggregation Network (NAN) for video face recognition. The network takes a face video or face image set of a person with a variable number of face images as its input, and produces a compact, fixed-dimension feature representation for recognition. The whole network is composed of two modules. The feature embedding module is a deep Convolutional Neural Network (CNN) which maps each face image to a feature vector. The aggregation module consists of two attention blocks which adaptively aggregate the feature vectors to form a single feature inside the convex hull spanned by them. Due to the attention mechanism, the aggregation is invariant to the image order. Our NAN is trained with a standard classification or verification loss without any extra supervision signal, and we found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces. The experiments on IJB-A, YouTube Face, Celebrity-1000 video face recognition benchmarks show that it consistently outperforms naive aggregation methods and achieves the state-of-the-art accuracy.