针对传统二维Fisher 线性判别(2DFLD)方法只使用图像矩阵的行向量作子模式的局限性，结合人耳图像的特点，提出了一种基于列向量作子模式的2DFLD 的人耳识别方法。首先利用训练样本图像矩阵的列向量作子模式进行训练以提取特征人耳子空间，再将测试图像投影到该子空间上，最后用最近邻欧式距离方法进行匹配。实验结果表明，以列向量作子模式时的识别率达98.333%，比行向量作子模式时提高了3.333%，与同样基于多元统计分析的PCA、2DPCA 和PCA+FLD 方法相比，识别效果最优，是一种有效的人耳识别方法。

To overcome the problem that the conventional algorithm based on Two-Dimensional Fisher Linear Discriminant (2DFLD) only took the row vectors of image matrix as sub-pattern, an ear recognition algorithm based on 2DFLD with taking the column vectors of image matrix as sub-pattern was proposed. Firstly, the ear feature subspace was extracted after processing training by using the column vectors of train image matrix as sub-pattern. Secondly, the test sample images were projected on small dimension subspace. Lastly, the nearest neighbor classifier to ear match based on Euclidean distance was used. The experimental results show that the recognition rate of column vectors reaches 98.333%, which is about 3.333% higher than that of row vectors. Compared with other methods such as PCA, 2DPCA and PCA+FLD based on the multi-element statistic analysis, the proposed method is the best one. It is an effective way of ear recognition.