Paper Info
Title
Classifier shared deep network with multi-hierarchy loss for low resolution face recognition.
Abstract
Face images in real Closed-Circuit Television (CCTV) are usually with low resolution, which remarkably deteriorates the performance of existing face recognition algorithms and hinders the application of face recognition. The main technical focus of this issue, matching between high-resolution (HR) and low-resolution (LR) face images has attracted significant attention. In order to better address this problem, we propose a Classifier Shared Deep Network with Multi-Hierarchy Loss (CS-MHL-Net) for low-resolution face recognition (LRFR) in this paper. Firstly, considering that contrastive loss and its variants are not conducive to the convergence of network and the reduction of discrepancy, a shared classifier between HR and LR face images is proposed to further narrow the domain gap between HR and LR by sharing the corresponding weights which can be seen as the class center. Secondly, to fully exploit intermediate features and loss constraints, we embed multi-hierarchy loss into intermediate layers, with the target of reducing the distances between HR and LR intermediate features after max pooling and avoiding the decreasing of accuracy caused by over-utilization of intermediate features. Experimental results on LFW and SCface demonstrate the effectiveness and superiority of the proposed method.
Year
DOI
Venue
2020
10.1016/j.image.2019.115766
Signal Processing: Image Communication
Keywords
Field
DocType
Low-resolution face recognition,Classifier shared deep network,Multi-hierarchy loss,Intermediate features
Convergence (routing),Computer vision,Facial recognition system,Pattern recognition,Computer science,Pooling,Exploit,Artificial intelligence,Classifier (linguistics),Hierarchy
Journal
Volume
ISSN
Citations 
82
0923-5965
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Jingna Sun120.74
Yehu Shen230.75
WM322134.28
QM446472.05