NDLI: Colour fusion in multi-spectral face authentication systems

Content Provider	IEEE Xplore Digital Library
Author	Tabatabaeifar, M.S. Sadeghi, M.T.
Copyright Year	2009
Description	Author affiliation: Department of Electronic Engineering, Islamic Azad University Mehriz Branch, Mehriz university, Iran (Tabatabaeifar, M.S.) \|\| Centre of Vision, university of Yazd, Yazd university, Iran (Sadeghi, M.T.)
Abstract	In this paper, the problem of multi-spectral face authentication is considered. The verification process is based on the Normalised Correlation measure within the LDA feature space. First, considering visible and thermal Infra Red (IR) images, different colour transformations are applied and the performance of the system within each colour space is evaluated. The scores associated to an adaptively selected subset of the colour based classifiers are then fused in the decision level. The selection process is based on a sequential search technique called the “plus L and take away R” algorithm. The sum rule is used for fusing the related scores. Our extensive experimental studies using the UTK-IRIS face database demonstrate that using the proposed method, the performance of the system considerably improves as compared to the individual Visible-based or IR-based face verification systems. The proposed multi-spectral colour fusion scheme also outperforms the best colour space in different conditions within the IR and Visible colour subspaces.
Starting Page	486
Ending Page	491
File Size	478837
Page Count	6
File Format	PDF
ISBN	9781424455607
e-ISBN	9781424455614
DOI	10.1109/ICSIPA.2009.5478704
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-18
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temperature sensors Face recognition Authentication Lighting Color Infrared image sensors Signal processing Optical imaging Linear discriminant analysis Face detection
Content Type	Text
Resource Type	Article

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