Feature extraction and recognition of iris based on ICA-MJE and SVM
基于ICA-MJE和SVM的虹膜特征提取与识别

A new method for iris feature extraction and recognition was proposed in this paper. Feature was extracted with independent component analysis by maximizing J-divergence entropy (ICA-MJE), and then Support Vector Machine (SVM) was used to match two iris codes. Compared with that of Gabor wavelet method, the size of an iris code and the processing time of the feature extraction were significantly reduced. Experimental results show that the developed system with high iris recognition rate could be used for a personal identification system in a more efficient and effective manner.