As a new biometrics authentication technology, ear recognition remains many unresolved problems, one of them is the
occlusion problem. This paper deals with ear recognition with partially occluded ear images. Firstly, the whole 2D image
is separated to sub-windows. Then, Neighborhood Preserving Embedding is used for feature extraction on each subwindow,
and we select the most discriminative sub-windows according to the recognition rate. Thirdly, a multi-matcher
fusion approach is used for recognition with partially occluded images. Experiments on the USTB ear image database
have illustrated that using only few sub-window can represent the most meaningful region of the ear, and the multimatcher
model gets higher recognition rate than using the whole image for recognition.