Paper Info
Title
Face matching between near infrared and visible light images
Abstract
In many applications, such as E-Passport and driver's license, the enrollment of face templates is done using visible light (VIS) face images. Such images are normally acquired in controlled environment where the lighting is approximately frontal. However, Authentication is done in variable lighting conditions. Matching of faces in VIS images taken in different lighting conditions is still a big challenge. A recent development in near infrared (NIR) image based face recognition [1] has well overcome the difficulty arising from lighting changes. However, it requires that enrollment face images be acquired using NIR as well. In this paper, we present a new problem, that of matching a face in an NIR image against one in a VIS images, and propose a solution to it. The work is aimed to develop a new solution for meeting the accuracy requirement of face-based biometric recognition, by taking advantages of the recent NIR face technology while allowing the use of existing VIS face photos as gallery templates. Face recognition is done by matching an NIR probe face against a VIS gallery face. Based on an analysis of properties of NIR and VIS face images, we propose a learning-based approach for the different modality matching. A mechanism of correlation between NIR and VIS faces is learned from NIR → VIS face pairs, and the learned correlation is used to evaluate similarity between an NIR face and a VIS face. We provide preliminary results of NIR → VIS face matching for recognition under different illumination conditions. The results demonstrate advantages of NIR → VIS matching over VIS → VIS matching.
Year
DOI
Venue
2007
10.1007/978-3-540-74549-5_55
ICB
Keywords
Field
DocType
nir face,vis face photo,face recognition,vis image,vis gallery face,nir probe face,vis matching,vis face pair,vis face,vis face image,visible light image,near infrared,canonical correlation analysis,dimension reduction,visible light
Face matching,Computer vision,Facial recognition system,Dimensionality reduction,Pattern recognition,Computer science,Near-infrared spectroscopy,Image based,Visible spectrum,Artificial intelligence,Biometrics
Conference
Volume
ISSN
ISBN
4642
0302-9743
3-540-74548-3
Citations 
PageRank 
References 
73
2.55
9
Authors
5
Name
Order
Citations
PageRank
Dong Yi1117343.66
Rong Liu2732.89
Rufeng Chu356027.44
Zhen Lei43613157.95
Stan Z. Li58951535.26