Wavelet based independent component analysis for palmprint identification
基于小波分解的独立分量掌纹识别方法

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.