说明：  首先计算掌纹图像二维Gabo r小波变 换系数的幅值, 将其作为掌纹图像的原始特征 其次, 利用 2DPCA 实现原始特征的降维 然后, 利用PCA 与FLD 的融合算 法进行二次降维的同时提取出最有利于分类的鉴别特征 最后, 利用最近邻算法进行掌纹的分类识别。
(First calculate the two-dimensional Gabo r palmprint image wavelet coefficients of magnitude, its characteristics as the original palmprint image Secondly, the use of original features 2DPCA realize dimension reduction then, the use of PCA and FLD fusion algorithm is a second drop dimension at the same time extract the most favorable classification identification features Finally, the nearest neighbor algorithm for palmprint classification.)