基于小波分解的独立分量掌纹识别方法

独立分量分析方法在图像处理中具有独特的优势，用于掌纹特征提取，使得变换后的各分量之间不仅互不相关，而且还尽可能的统计独立，能更全面的揭示掌纹特征间的本质结构。为了降低运算复杂度，提出了一种基于小波分解的独立分量掌纹特征提取方法。首先对掌纹图像做小波变换进行降维，在保留原始图像轮廓信息和细节信息的基础上，去掉高频噪声，然后进行独立分量分析，采用FastICA算法，试验结果表明,本方法比传统的独立分量分析方法的识别率更高，且计算量大大减少。

Wavelet based independent component analysis for palmprint identification

Independent Component Analysis (ICA) has particular advantages in image processing.Used for palmprint feature extraction,it can ensure the components to be irrelevant and statistical independent among each other,and can more roundly describe the essential characteristics in palmprint features.In order to decrease the calculation of complication,the wavelet based independent component analysis for palmprint identification method was proposed.First,the palmprint images were decomposed into wavelet subimages,in which the profile and the minutia information were reserved,and the dimensions were decreased at the same time.Then take the wavelet subimages as the input of ICA,by using FastICA algorithm,the experimental results show that the integrated method is more efficient than ICA algorithm in accuracy and speed.