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