PA-GAN: A Patch-Attention Based Aggregation Network for Face Recognition in Surveillance

Ming Liu,Qingbao Li,Jinjin Liu,Ping Zhang

doi:10.1109/access.2020.3017779

Abstract

Face recognition in unconstraint surveillance is a complicated problem on account of motion blur, expression variations and low resolution. Recent works have demonstrated that patch-attention is strictly more powerful than convolution in recognition models. In this study, we investigate the task of unconstraint surveillance face recognition. First, a Patch-Attention Generative Adversarial Network (PA-GAN) model is devised to aggregate some robust features on behalf of a set of raw surveillance frames, which not only increases the recognition accuracy but also reduces the computational costs of face matching. Second, an improved center loss function combined with abundant unlabeled surveillance faces is utilized to accurately classify the known identities. With the proposed method, the discriminativeness of the face representations is largely enhanced. Finally, the proposed method is verified in two widely used datasets, IJB-A dataset and QMUL-SurvFace dataset to demonstrate the effectiveness. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed design can achieve competitive accuracy on both the verification and identification protocols.

Highlights

During recent decades, video-based face recognition (FR) has received considerable attention in both academia and industry due to its wide range of various security systems and law enforcement applications
The main contributions of this article can be summarized as follows: We propose an efficient Patch-Attention Generative Adversarial Network (PA-DAN) which aggregates each frame adaptively to a few representations for surveillance face recognition
As an illustration from the results, we find that LDis is more powerful than LRec, increased by 1.25% and 1.10% respectively. 3) setting the PANet-15 as the backbone, the recognition accuracy has been significantly improved by 2%

Summary

Introduction

Video-based face recognition (FR) has received considerable attention in both academia and industry due to its wide range of various security systems and law enforcement applications. One most significant thing is the successful use of the face recognition technology by public security systems to arrest escaping criminals and search for missing person. How to quickly and accurately identify the unique information of enormous faces in videos is of great significance to the development of security field. The compelling progress in deep learning and computer vision, it is still a great challenge to match surveillance face images in different modalities, especially in open-set scenario [1]. Due to the considerable discrepancy between source and the target domains, one challenge is that the face recognition model trained on unconstrained

Objectives

Results

Conclusion