Paper Info
Title
Towards resolution invariant face recognition in uncontrolled scenarios
Abstract
Face images captured by surveillance cameras usually have poor quality, particularly low resolution (LR), which affects the performance of face recognition seriously. In this paper, we develop a novel approach to address the problem of matching a LR face image against a gallery of relatively high resolution (HR) face images. Existing methods deal with such cross-resolution face recognition problem either by importing the information of HR images to help synthesize HR images from LR images or by applying the discrimination of HR images to help search for a unified feature space. Instead, we treat the discrimination information of HR and LR face images equally to boost the performance. The proposed approach learns resolution invariant features aiming to: (1) classify the identity of both LR and HR face images accurately, and (2) preserve the discriminative information among subjects across different resolutions. We conduct experiments on databases of uncontrolled scenarios, i.e., SCface and COX, and results show that the proposed approach significantly outperforms state-of-the-art methods.
Year
DOI
Venue
2016
10.1109/ICB.2016.7550087
2016 International Conference on Biometrics (ICB)
Keywords
Field
DocType
resolution invariant face recognition,uncontrolled scenarios,surveillance cameras,LR face image matching,high resolution face images,cross-resolution face recognition,HR image classification,LR image classification,unified feature space,discrimination information,COX,SCface,face image capture
Computer vision,Facial recognition system,Feature vector,Object-class detection,Pattern recognition,Three-dimensional face recognition,Computer science,Feature extraction,Artificial intelligence,Face detection,Image resolution,Discriminative model
Conference
ISSN
ISBN
Citations 
2376-4201
978-1-5090-1870-3
0
PageRank 
References 
Authors
0.34
25
3
Name
Order
Citations
PageRank
Dan Zeng100.34
Hu Chen215017.55
Qijun Zhao341938.37