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Summary: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 4, NO. 1, MARCH 2009 111
Improving Face Recognition via Narrowband Spectral
Range Selection Using Jeffrey Divergence
Hong Chang, Member, IEEE, Yi Yao, Andreas Koschan, Member, IEEE, Besma Abidi, and Mongi Abidi
Abstract--In order to achieve improved recognition perfor-
mance in comparison with conventional broadband images, this
paper addresses a new method that automatically specifies the
optimal spectral range for multispectral face images according to
given illuminations. The novelty of our method lies in the intro-
duction of a distribution separation measure and the selection of
the optimal spectral range by ranking these separation values. The
selected spectral ranges are consistent with the physics analysis of
the multispectral imaging process. The fused images from these
chosen spectral ranges are verified to outperform the conventional
broadband images by 3%20%, based on a variety of experiments
with indoor and outdoor illuminations using two well-recognized
face-recognition engines. Our discovery can be practically used
for a new customized sensor design associated with given illu-
minations for improved face-recognition performance over the
conventional broadband images.
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