Ethnicity identification of face images is of interest in many areas of application. Different from face recognition of
individuals, ethnicity identification classifies faces according to the common features of a specific ethnic group. This
paper presents a multi-level fusion scheme for ethnicity identification that combines texture features of local areas of a
face using local binary patterns with color features using HSV binning. The scheme fuses the decisions from a k-nearest
neighbor classifier and a support vector machine classifier into a final identification decision. We have tested the scheme
on a collection of face images from a number of publicly available databases. The results demonstrate the effectiveness
of the combined features and improvements on accuracy of identification by the fusion scheme over the identification
using individual features and other state-of-art techniques.