Abstract: Ethnicity identification of face images is of interest in
many areas of application, but existing methods are few and limited.
This paper presents a fusion scheme that uses block-based uniform
local binary patterns and Haar wavelet transform to combine local
and global features. In particular, the LL subband coefficients of the
whole face are fused with the histograms of uniform local binary
patterns from block partitions of the face. We applied the principal
component analysis on the fused features and managed to reduce the
dimensionality of the feature space from 536 down to around 15
without sacrificing too much accuracy. We have conducted a number
of preliminary experiments using a collection of 746 subject face
images. The test results show good accuracy and demonstrate the
potential of fusing global and local features. The fusion approach is
robust, making it easy to further improve the identification at both
feature and score levels.