Title:
[first_author]_2014_Digital-Signal-Processing
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Description: This study proposes a novel near infrared face recognition algorithm based on a combination of both
local and global features. In this method local features are extracted from partitioned images by means
of undecimated discrete wavelet transform (UDWT) and global features are extracted from the whole
face image by means of Zernike moments (ZMs). Spectral regression discriminant analysis (SRDA) is then
used to reduce the dimension of features. In order to make full use of global and local features and
further improve the performance, a decision fusion technique is employed by using weighted sum rule.
Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that the proposed
method has superior overall performance compared to some other methods in the presence of facial
expressions, eyeglasses, head rotation, image noise and misalignments. Moreover its computational time
is acceptable for on-line face recognition systems
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