Ear Recognition Based on Wavelet Decomposition and Discriminative Common Vector
赵海龙 Zhao Hailong, Zhichun Mu, Xia Zhang, Dun Wen-jie · Jisuanji gongcheng · 2009
LDA is widely used for linear dimension reduction.However,LDA has some limitations that one of the scatter matrices is required to be nonsingular.Discriminative Common Vector(DCV) is one of the most successful methods which overcomes the problem caused by the singularity of the scatter matrices.But when DCV is directly used to reduce the dimension of the ear images,the computational expense of training is still relatively large.A new method is proposed in this paper that the low frequency subimages are obtained by utilizing two-dimensional wavelet transform and the features are extracted by applying DCV to the subimages.The experimental results show that the proposed method achieves better performance than Fisher face method and the computation burden is also reduced greatly.