主成分分析重建误差掌纹识别方法

主成分分析(PCA)法在掌纹识别方面可以取得较好的效果.但是随着掌纹图像库的扩大,PCA转换矩阵训练时间迅速增长;注册新掌纹时,需要重新训练PCA转换矩阵.添加注册掌纹的代价随着掌纹库的增大迅速增加.如何能够在保持PCA识别效果的情况下提高使用的便捷性成为PCA广泛应用的主要障碍.提出了一种以PCA重建误差为分类依据的PCA重建误差学纹识别方法.该方法与PCA法基于相同的原理,在采用最近邻分类器时可以取得与PCA法相等的性能;同时可以有效减少掌纹图像库的识别时间,可以以极少的代价扩展掌纹库.

PCA Reconstruction Error Palmprint Recognition

Principal component analysis(PCA) is very effective in palmprint recognition.However,with enlargement of the palmprint image library,the training time of the transforming matrix increases rapidly.When new palmprintis added in,the PCA transforming matrix should be retrained,so the cost of adding new palmprint to the libarary increases rapidly with the enlargement of the palmprint image libarary.It has been a major obstacle to the extensive use of PCA that how to make PCA easy to use,meanwhile the recognition...