Extraction and expression of features are critical to improve the recognition rate of ear image recognition. Scale invariant feature transform (SIFT) is a local point features extraction method. It can find those feature vectors in different scale spaces which are invariant for scale changes and rotations and flexible for illumination variations and affine transformations. SIFT is used to extract structural feature points of ear images and get stable feature descriptors. In order to overcome a defect of local descriptor that an image may have multiple similar regions, an auricle geometric feature is fused. Ear recognition based on these fusion vectors is carried out by using Euclid distance as similarity measurement. Experimental results show that the proposed method can effectively extract ear feature points and obtain high recognition ratio by using few feature points. It is robust to rigid transformation of ear image.