A new algorithm of ear identification is proposed in this paper aiming at the rotating distortion problem.First,the ear gray-scale image is divided into several sub-images,and then these sub-images are equally divided into several sub-regions.The location information in sub-region which contains the maximum gray-scale sum of all points in this region,will build the feature matrix of the ear image.At last,it will realize recognition by matching the feature matrix in space directly.We test the algorithm with the databases of USTB.The experimental results indicate that the recognition accuracy of the proposed algorithm is up to 98.89% for ±30°rotation images compared with the feature extraction methods of block Fourier transformation and block mean and standard variance,so the algorithm has more strong robustness for the rotating-distortion ear images.Furthermore,the system response time is only 21.33 ms,so it is an effective ear identification algorithm with high practicability.