Ear recognition based on local information fusion

Abstract

Ears have rich structural features that are almost invariant with increasing age and facial expression variations. Therefore ear recognition has become an effective and appealing approach to non-contact biometric recognition. This paper gives an up-to date review of research works on ear recognition. Current 2D ear recognition approaches achieve good performance in constrained environments. However the recognition performance degrades severely under pose, lighting and occlusion. This paper proposes a 2D ear recognition approach based on local information fusion to deal with ear recognition under partial occlusion. Firstly, the whole 2D image is separated to sub-windows. Then, Neighborhood Preserving Embedding is used for feature extraction on each sub-window, and we select the most discriminative sub-windows according to the recognition rate. Each sub-window corresponds to a sub-classifier. Thirdly, a sub-classifier fusion approach is used for recognition with partially occluded images. Experimental results on the USTB ear dataset and UND dataset have illustrated that using only few sub-windows we can represent the most meaningful region of the ear, and the multi-classifier model gets higher recognition rate than using the whole image for recognition.