Ear Recognition Based on Two-dimensional Fisher Linear Discriminant
Ke Li · Guangdian gongcheng · 2009
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.