Locality Preserving Projections (LPP) algorithm constructs the neighbor graph using Euclidean distance, which may not reflect the actual distribution relationship of the image data in high dimensional space. In order to solve the problem, a new algorithm of locality preserving projections fused with correlation coefficient is proposed. The algorithm uses Euclidean distance fused with correlation coefficient of ear images to construct the neighbor graph, which can reflect the geometry relationship of the image data better. Meanwhile, we also take correlation coefficient into account when we calculate the weights of the neighbor graph, thus reflect the similarity relationship of data, extract the distinguishing features, and realize the data dimension reduction. The results of the comparative experiment of the two methods on USTB3 and Spain ear database indicate that the highest accuracy of the proposed method is improved over 10%, which verifies the efficiency of the proposed algorithm.