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Ear recognition under partial occlusion based on neighborhood preserving embedding

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.