NDLI: Fusion of visible and synthesised near infrared information for face authentication

Content Provider	IEEE Xplore Digital Library
Author	Mavadati, S.M. Sadeghi, M.T. Kittler, J.
Copyright Year	2010
Description	Author affiliation: Signal Processing Research Lab., Department of Electrical & Computer Engineering, Yazd University, Iran (Mavadati, S.M.; Sadeghi, M.T.) \|\| CVSSP, University of Surrey, Guildford, GU2 7XH Surrey, U.K. (Kittler, J.)
Abstract	Changes in illumination conditions can cause drastic variations in face appearance and affect the performance of a face authentication system. Near infrared (NIR) face imaging systems have been proposed as a promising way towards illumination invariant face verification. We show that when NIR face images cannot be observed, learning the relationship between NIR information and the corresponding visible images can provide useful complementary information about visible light image data. In particular, we use Canonical Correlation Analysis (CCA) to synthesise the NIR eigenfaces from their corresponding visible ones. In this paper, the verification performance of a CCA-based synthesising algorithm is developed first. Although, synthesised NIR images do not perform as well as the real NIR, it is shown that by fusing the visible and synthesised near infrared information at the score level, the performance of the authentication system considerably improves.
Starting Page	3801
Ending Page	3804
File Size	507245
Page Count	4
File Format	PDF
ISBN	9781424479924
ISSN	15224880
e-ISBN	9781424479948
e-ISBN	9781424479931
DOI	10.1109/ICIP.2010.5654024
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-26
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face Lighting Correlation Support vector machines Authentication Databases Linear regression Score Level Fusion Face Verification Near Infrared Canonical Correlation Analysis
Content Type	Text
Resource Type	Article

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